Vision system with tail detection

ABSTRACT

A system that includes a three-dimensional (3D) camera configured to capture a 3D image of a rearview of a dairy livestock in a stall and a processor. The processor is configured to obtain the 3D image, identify one or more regions within the 3D image comprising depth values greater than a depth value threshold, and s to identify a thigh gap region from the one or more regions. The processor is further configured to demarcate an access region within the thigh gap region and demarcate a tail detection region. The processor is further configured to identify one or more tail candidates within the tail detection region, to identify a tail candidate that corresponds with a tail model as the tail, and to determine position information for the tail.

RELATED APPLICATION

This application is a continuation of pending U.S. patent applicationSer. No. 15/239,425 filed Aug. 17, 2016 entitled “Vision System withTail Detection,” which is now U.S. Pat. No. 9,984,470 issued May 29,2018.

TECHNICAL FIELD

This disclosure relates generally to dairy farming, and morespecifically, to a vision system for facilitating operations on a dairylivestock.

BACKGROUND

Over time, the size and complexity of dairy milking operations hasincreased. Accordingly, the need for efficient processes and systemsthat support dairy milking operations has also increased. However,existing solutions for supporting dairy milking operations have proveninadequate in various respect.

SUMMARY

In one embodiment, the disclosure includes a system that includes athree-dimensional (3D) camera configured to capture a 3D image of arearview of a dairy livestock in a stall. The dairy livestock isoriented in the 3D image with respect to an x-axis corresponding with ahorizontal dimension of the 3D image, a y-axis corresponding with avertical dimension of the 3D image, and a z-axis corresponding withdepth dimension into the 3D image. Each pixel of the 3D image isassociated with a depth value along the z-axis. The system furtherincludes a memory operable to store a thigh gap detection rule set and aprocessor operably coupled to the 3D camera and the memory. Theprocessor is configured to obtain the 3D image, identify one or moreregions within the 3D image comprising depth values greater than a depthvalue threshold, and apply the thigh gap detection rule set to the oneor more regions to identify a thigh gap region among the one or moreregions. The thigh gap region comprises an area between hind legs of thedairy livestock. The processor is further configured to demarcate anaccess region within the thigh gap region that is defined by a firstvertical edge, a second vertical edge, a first upper edge spanningbetween the first vertical edge and the second vertical edge, and afirst lower edge spanning between the first vertical edge and the secondvertical edge. The processor is further configured to demarcate a taildetection region that is defined by a third vertical edge extendingvertically from the first vertical edge of the access region, a fourthvertical edge extending vertically from the second vertical edge of theaccess region, a second upper edge spanning between the third verticaledge and the fourth vertical edge, and a second lower edge spanningbetween the third vertical edge and the fourth vertical edge. The secondlower edge is adjacent to the first upper edge of the access region. Theprocessor is further configured to partition the 3D image within thetail detection region along the z-axis to generate a plurality of imagedepth planes and examine each of the plurality of image depth planes.Examining each of the plurality of image depth planes comprisesidentifying one or more tail candidates within the image depth plane,comparing the one or more tail candidates to a tail model, discardingtail candidates that do not correspond with the tail model, andidentifying a tail candidate from among the one or more tail candidatesas a tail of the dairy livestock when the tail candidate correspondswith the tail model. The processor is further configured to determineposition information for the tail of the dairy livestock in response toidentifying the tail of the dairy livestock.

In another embodiment, the disclosure includes an apparatus thatincludes a memory operable to store a thigh gap detection rule set and aprocessor operably coupled to the memory. The processor is configured toobtain a 3D image of a rearview of a dairy livestock in a stall. Thedairy livestock is oriented in the 3D image with respect to an x-axiscorresponding with a horizontal dimension of the 3D image, a y-axiscorresponding with a vertical dimension of the 3D image, and a z-axiscorresponding with depth dimension into the 3D image. Each pixel of the3D image is associated with a depth value along the z-axis. Theprocessor is further configured to identify one or more regions withinthe 3D image comprising depth values greater than a depth valuethreshold and apply the thigh gap detection rule set to the one or moreregions to identify a thigh gap region among the one or more regions.The thigh gap region comprises an area between hind legs of the dairylivestock. The processor is further configured to demarcate an accessregion within the thigh gap region that is defined by a first verticaledge, a second vertical edge, a first upper edge spanning between thefirst vertical edge and the second vertical edge, and a first lower edgespanning between the first vertical edge and the second vertical edge.The processor is further configured to demarcate a tail detection regionthat is defined by a third vertical edge extending vertically from thefirst vertical edge of the access region, a fourth vertical edgeextending vertically from the second vertical edge of the access region,a second upper edge spanning between the third vertical edge and thefourth vertical edge, and a second lower edge spanning between the thirdvertical edge and the fourth vertical edge. The second lower edge isadjacent to the first upper edge of the access region. The processor isfurther configured to partition the 3D image within the tail detectionregion along the z-axis to generate a plurality of image depth planesand examine each of the plurality of image depth planes. Examining eachof the plurality of image depth planes comprises identifying one or moretail candidates within the image depth plane, comparing the one or moretail candidates to a tail model, discarding tail candidates that do notcorrespond with the tail model, and identifying a tail candidate fromamong the one or more tail candidates as a tail of the dairy livestockwhen the tail candidate corresponds with the tail model. The processoris further configured to determine position information for the tail ofthe dairy livestock in response to identifying the tail of the dairylivestock.

In yet another embodiment, the disclosure includes a tail detectionmethod that includes obtaining a 3D image of a rearview of a dairylivestock in a stall. The dairy livestock is oriented in the 3D imagewith respect to an x-axis corresponding with a horizontal dimension ofthe 3D image, a y-axis corresponding with a vertical dimension of the 3Dimage, and a z-axis corresponding with depth dimension into the 3Dimage. Each pixel of the 3D image is associated with a depth value alongthe z-axis. The method further includes identifying one or more regionswithin the 3D image comprising depth values greater than a depth valuethreshold and applying a thigh gap detection rule set to the one or moreregions to identify a thigh gap region among the one or more regions.The thigh gap region comprises an area between hind legs of the dairylivestock. The method further includes demarcating an access regionwithin the thigh gap region that is defined by a first vertical edge, asecond vertical edge, a first upper edge spanning between the firstvertical edge and the second vertical edge, and a first lower edgespanning between the first vertical edge and the second vertical edge.The method further includes demarcating a tail detection region that isdefined by a third vertical edge extending vertically from the firstvertical edge of the access region, a fourth vertical edge extendingvertically from the second vertical edge of the access region, a secondupper edge spanning between the third vertical edge and the fourthvertical edge, and a second lower edge spanning between the thirdvertical edge and the fourth vertical edge. The second lower edge isadjacent to the first upper edge of the access region. The methodfurther includes partitioning the 3D image within the tail detectionregion along the z-axis to generate a plurality of image depth planesand examining each of the plurality of image depth planes. Examiningeach of the plurality of image depth planes comprises identifying one ormore tail candidates within the image depth plane, comparing the one ormore tail candidates to a tail model, discarding tail candidates that donot correspond with the tail model, and identifying a tail candidatefrom among the one or more tail candidates as a tail of the dairylivestock when the tail candidate corresponds with the tail model. Themethod further includes determining position information for the tail ofthe dairy livestock in response to identifying the tail of the dairylivestock.

The present disclosure presents several technical advantages. Forexample, a vision system allows the robotic arm to detect and tocompensate for leg movement by a dairy livestock in about real time andwithout requiring hard coding movements and positions. As anotherexample, the vision system allows the robotic arm to detect and tocompensate for teat movement by the dairy livestock in about real timeand without requiring hard coding movements and positions. As anotherexample, the vision system allows the robotic arm to detect and to avoidthe tail of the dairy livestock when positioning the robot and/orperforming operations on the dairy livestock, which allows the roboticarm to position itself and to make adjustment in about real time toavoid the tail of the dairy livestock. As another example, the visionsystem allows the robotic arm to determine the identify of unknown teatsor to confirm the identity of teats while performing operations on thedairy livestock.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a vision system;

FIG. 2 is a side view of an embodiment of a dairy livestock and arobotic arm employing the vision system;

FIG. 3 is a perspective view of an embodiment of a plurality of imagedepth planes in a 3D image of a dairy livestock;

FIG. 4 is an embodiment of a three-dimensional (3D) image of a rearviewof a dairy livestock in a stall;

FIG. 5 is a flowchart of an embodiment of a leg detection method usingthe vision system;

FIG. 6 is a flowchart of an embodiment of a teat detection method usingthe vision system with a 3D image;

FIG. 7 is another embodiment of a 3D image of a rearview of a dairylivestock in a stall;

FIG. 8 is a flowchart of an embodiment of a tail detection method usingthe vision system;

FIG. 9 is a graph of an embodiment of a profile signal of a portion of adairy livestock;

FIG. 10 is a flowchart of an embodiment of a teat detection method usingthe vision system with a profile signal;

FIG. 11A is a position map of an embodiment of a plurality of teats of adairy livestock;

FIG. 11B is a position map of another embodiment of a plurality of teatsof a dairy livestock;

FIG. 12A is a flowchart of an embodiment of a teat identification methodusing the vision system;

FIG. 12B is a flowchart of another embodiment of a teat identificationmethod;

FIG. 13A is an embodiment of a comparison between a teat model and afeature of a dairy livestock in an image depth plane without a match;

FIG. 13B is an embodiment of a comparison between a teat model and afeature of a dairy livestock in an image depth plane with a match;

FIG. 14A is an embodiment of a comparison between a tail model and atail candidate in an image depth plane without a match;

FIG. 14B is an embodiment of a comparisons between a tail model and atail candidate in an image depth plane with a match;

FIG. 15 is an embodiment of a comparison between a teat model and edgepairs in a profile signal; and

FIG. 16 is a position map of an embodiment of teat candidate clustersfor teats of dairy livestock.

DETAILED DESCRIPTION

In the dairy industry, collecting milk from dairy animals such as cowsis an important part of dairy farming. The process of collecting milktypically involves positioning a robotic arm with a teat cup (e.g. ateat prepping cup or a milking cup) in a region adjacent to the teats ofthe cow. The robotic arm may enter and exit the region adjacent to thecow numerous times during the milking process, for example, to attachand to remove the teat cups from the cow. Due to the repetitive natureof this process, it is advantageous to automate this milking process.However, accurately positioning and maneuvering the robotic arm presentsa myriad of problems. For instance, cows may not remain in a fixedposition within a stall during the milking process, and thus, hardcoding specific movements and/or positions is ineffective. The teats ofa cow may not be in the exact same position on every cow, and thus, hardcoding specific teat positions is also is ineffective. Other features ofthe cow (e.g. the tail) may cause problems when trying to position arobotic arm and/or when trying to perform operations on the cow such asattaching a teat cup.

Disclosed herein are various embodiments of a vision system thataddresses several of these challenges. In one embodiment, a visionsystem may be configured to provide the ability to perform leg detectionusing three-dimensional (3D) images. The functionality and performanceof an autonomous system, such as a robotic arm system, may be improvedwhen the vision system is configured to provide leg detectioncapabilities. For example, the accuracy and speed of a robotic arm maybe increased when positioning or maneuvering the robotic arm adjacent tothe cow while safely avoiding the legs of a dairy livestock. The visionsystem allows the robotic arm to detect and to compensate for legmovement by a dairy livestock. The vision system may allow the roboticarm to position itself and to make adjustment in about real time, andthus hard coding is not required.

In another embodiment, a vision system may be configured to provide theability to perform teat detection using 3D images. The functionality andperformance of the robotic arm system may also be improved when thevision system is configured to provide the ability to perform teatdetection using 3D images. The accuracy and speed of a robotic arm maybe increased when locating and/or performing operations on the teats ofa dairy livestock. Using 3D images, the vision system allows the roboticarm to detect and to compensate for teat movement by the dairylivestock. The vision system may allow the robotic arm to positionitself and to make adjustment in about real time, and thus hard codingis not required.

In another embodiment, a vision system may be configured to provide theability to perform teat detection using profile signals. Similarly, thefunctionality and performance of the robotic arm system may also beimproved when the vision system is configured to provide the ability toperform teat detection using profile signals. The accuracy and speed ofa robotic arm may be increased when locating and/or performingoperations on the teats of a dairy livestock. Using profile signals, thevision system allows the robotic arm to detect and to compensate forteat movement by the dairy livestock.

In another embodiment, a vision system may be configured to provide theability to perform tail detection using 3D images. The functionality andperformance of the robotic arm system may also be improved when thevision system is configured to provide the ability to perform taildetection using 3D images. The accuracy and speed of a robotic arm maybe increased when performing operations on the teats of a dairylivestock. The vision system allows the robotic arm to detect and toavoid the tail of the dairy livestock when positioning the robot and/orperforming operations on the dairy livestock. The vision system mayallow the robotic arm to position itself and to make adjustment in aboutreal time to avoid the tail of the dairy livestock.

In another embodiment, a vision system may be configured to provide theability to perform teat identification. The functionality andperformance of the robotic arm system may also be improved when thevision system is configured to provide the ability to perform taildetection using 3D images. The accuracy and speed of a robotic arm maybe increased when performing operations on the teats of a dairylivestock. The vision system allows the robotic arm to determine theidentify of unknown teats or to confirm the identity of teats whileperforming operations on the dairy livestock.

The present disclosure will be described in more detail using FIGS.1-15. FIG. 1 illustrates a general overview of a vision system forfacilitating the positioning and maneuvering of a robotic arm. FIG. 2illustrates positioning a robotic arm adjacent to a dairy livestock.FIG. 3 illustrates features of a 3D image. FIGS. 4 and 7 illustrateexamples of 3D images. FIGS. 5, 6, and 8 illustrate examples of methodsof using 3D images to identify features of a dairy livestock. FIG. 9illustrates an example of a profile signal. FIG. 10 illustrates anexample of a method of using a profile signal to identify features of adairy livestock. FIGS. 11A and 11B illustrate examples of position mapsof teats of a dairy livestock. FIG. 12A illustrates an example of amethod for identifying an unknown teat using teat location information.FIG. 12B illustrates an example of a method for associating teatcandidates with teats of a dairy livestock. FIGS. 13A and 13Billustrates an example of using a teat model to identify teats of adairy livestock in an image depth plane. FIGS. 14A and 14B illustratesan example of using a tail model to identify a tail of a dairy livestockin an image depth plane. FIG. 15 illustrates another example of using ateat model to identify teats of a dairy livestock in a profile signal.FIG. 16 illustrates using teat candidate clusters to determine thelocation of teats of a dairy livestock.

FIG. 1 is a schematic view of an embodiment of a vision system 100. Thevision system 100 may be configured to obtain visual information of adairy livestock and to determine information associated with thefeatures of the dairy livestock based on the visual information.Examples of dairy livestock include, but are not limited to, cows,buffalos, goats, or any other suitable animal. The vision system 100 maybe configured to capture and to process visual information of the dairylivestock in order to facilitate one or more operations on the dairylivestock. Processing the visual information may include, but is notlimited to, performing leg detection, performing tail detection,performing teat detection, and performing teat identification. Forexample, the vision system 100 may be configured to detect and todetermine the location of one or more legs of a dairy livestock. Anexample of a leg detection method is described in FIG. 5. The visionsystem 100 may be configured to detect and to determine the location ofteats of the dairy livestock. An example of teat detection methods aredescribed in FIGS. 6 and 11. The vision system 100 may be configured todetect and to determine the location of a tail of the dairy livestock.An example of a tail detection method is described in FIG. 8. The visionsystem 100 may be also be configured to identify one or more teats ofthe dairy livestock. An example of a teat identification method isdescribed in FIG. 12A.

The vision system 100 may comprise a processor 102, a memory 104, anetwork interface 106, a user interface 108, an input/output (I/O)interface 110, and visioning devices 112. The vision system 100 may beconfigured as shown or in any other suitable configuration.

The processor 102 may be implemented as one or more central processingunit (CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), or digital signal processors (DSPs). The processor 102is communicatively coupled to and in signal communication with thememory 104, the network interface 106, the user interface 108, the I/Ointerface 110, the visioning devices 112, and a robotic arm system 140.The processor 102 is configured to receive and transmit electricalsignals among one or more of the memory 104, the network interface 106,the user interface 108, the I/O interface 110, and the visioning devices112. The electrical signals may be used to send and receive data (e.g.profile signals 134 and 3D images 138) or to control other devices (e.g.visioning devices 112 and robotic arm system 140). For example, theprocessor 102 may transmit electrical signals to operate one the laser132 and/or the 3D camera 136. The processor 102 may be operably coupledto one or more other devices (not shown).

The processor 102 is configured to process data and may be implementedin hardware or software. The processor 102 may be configured toimplement various instructions. For example, the processor 102 may beconfigured to implement leg detection instructions 116, teat detectioninstructions 118, tail detection instructions 120, and teatidentification instructions 122. In FIG. 1, leg detection instructions116, teat detection instructions 118, tail detection instructions 120,and teat identification instructions 122 are implemented as instructions(e.g. software code or firmware) stored in the memory 104. In otherembodiments, leg detection instructions 116, teat detection instructions118, tail detection instructions 120, and/or teat identificationinstructions 122 may be implemented as instructions stored in theprocessor 102. The processor 102 may be configured to implement legdetection instructions 116, teat detection instructions 118, taildetection instructions 120, and/or teat identification instructions 122in about real time. The inclusion of leg detection instructions 116,teat detection instructions 118, tail detection instructions 120, and/orteat identification instructions 122 provide an improvement to thefunctionality of the vision system 100, which effects a transformationof the vision system 100 to a different state.

The memory 104 may comprise one or more disks, tape drives, orsolid-state drives, and may be used as an over-flow data storage device,to store programs when such programs are selected for execution, and tostore instructions and data that are read during program execution. Thememory 104 may be volatile or non-volatile and may comprise read-onlymemory (ROM), random-access memory (RAM), ternary content-addressablememory (TCAM), dynamic random-access memory (DRAM), and staticrandom-access memory (SRAM). The memory 104 is operable to store legdetection instructions 116, teat detection instructions 118, taildetection instructions 120, teat identification instructions 122, teatdetection rule sets 124, depth value thresholds 125, thigh gap detectionrule sets 126, livestock information database 114, teat models 128, tailmodels 129, teat location information 130, and/or any other data, rulesets, models, or instructions. The leg detection instructions 116, teatdetection instructions 118, tail detection instructions 120, and teatidentification instructions 122 may be implemented by the processor 102to execute instructions for processing visual information of a dairylivestock. For example, executing the leg detection instructions 116 mayconfigure the processor 102 to detect one or more legs of a dairylivestock and to determine position information for the legs of thedairy livestock. An example of executing the leg detection instructions116 is described in FIG. 5. Executing the teat detection instructions118 may configure the processor 102 to detect one or more teats of adairy livestock and to determine position information for the one ormore teats, for example, with respect to a robotic arm. Examples ofexecuting the teat detection instructions 118 are described in FIGS. 6and 10. Executing the tail detection instructions 120 may configure theprocessor 102 to detect a tail of a dairy livestock and to determineposition information for the tail of the dairy livestock. An example ofexecuting the tail detection instructions 120 is described in FIG. 8.Executing the teat identification instructions 122 may configure theprocessor 102 to determine the identity of an unknown teat on a dairylivestock. An example of executing the teat identification instructions122 is described in FIG. 12A.

The vision system 100 may be configured to use depth value thresholds125 to identify regions within 3D images 138 with a particular depthinto the 3D image 138. A depth value threshold 125 may be a color,intensity, numeric value, or any other suitable indicator of aparticular depth in the 3D image 138. For example, the vision system 100may compare portions of the 3D image 138 to the depth value threshold125 and to identify one or more regions in the 3D image 138 thatcomprise depth values greater than the depth value threshold 125.Examples of using depth value thresholds 125 are described in FIGS. 4-8.

Teat detection rule sets 124 comprise one or more rules that may be usedfor identifying teats of a dairy livestock. In one embodiment, rulesfrom a teat detection rule set 124 may be applied to edge pairs in aprofile signal 134 to determine whether an edge pair is teat candidatefor a dairy livestock. Examples of using a teat detection rule set 124to identify teat candidates in a profile signal 134 are described inFIGS. 9, 10, and 15. In another embodiment, rules from a teat detectionrule set 124 may be applied to a teat candidate from a 3D image 138 todetermine whether the teat candidate is a teat on a dairy livestock.Examples of using the teat detection rule set 124 to identify teats on adairy livestock in a 3D image 138 are described in FIGS. 4, 6, 13A, and13B. Examples of rules in a teat detection rule set 124 include, but arenot limited to, using a teat model 130 to identify a teat candidates, aminimum or maximum complementary distance gradient spacing to beconsidered a teat candidate, a minimum or maximum distance gradientlength to be considered a teat candidate, a minimum or maximum area tobe considered a teat, a minimum or maximum height position to beconsidered a teat, and a minimum or maximum width to be considered ateat.

Thigh gap detection rule sets 126 comprise one or more rules that may beused for identifying a thigh gap region from among a plurality ofregions in a 3D image 138. For example, rules from a thigh gap detectionrule set 126 may be applied to one or more regions in a 3D image 138 todiscard regions that are not a thigh gap region and to identify a thighgap region from among the one or more regions. Examples of rules in athigh gap detection rule set 126 include, but are not limited to, usingmarkers (e.g. predetermined pixel locations) to identify a thigh gapregion, using boundaries of or within a 3D image 138 to identify a thighgap region, a minimum or maximum area to be considered a thigh gapregion, a minimum or maximum height to be considered a thigh gap region,and a minimum or maximum width to be considered a thigh gap region.Examples of using a thigh gap detection rule set 126 to identify a thighgap region are described in FIGS. 4-8.

The livestock information database 114 may be configured to storeinformation associated with one or more dairy livestock. Examples ofinformation stored in the livestock information database 114 include,but are not limited to, identification information for dairy livestock,historical information associated with dairy livestock, teat identifiers127, teat models 128, tail models 129, teat location information 130,and position maps 131. In one embodiment, the livestock informationdatabase 114 may be external to the vision system 100. For example, thelivestock information database 114 may be in a different geographiclocation than the vision system 100 and may be communicatively coupledto the vision system 100 using a network connection. In anotherembodiment, the livestock information database 114 may be integratedwith the vision system 100.

Teat identifiers 127 may be associated with and uniquely identify teats203 of a dairy livestock. An example of a teat identifier 127 includes,but is not limited to, an alphanumeric label. For example, a teatidentifier 127 may be a numeric value, a descriptive name (e.g. rightfront teat), or an alias. A teat identifier 127 may be represented usingany suitable structure, form, or format as would be appreciated by oneof ordinary skill in the art upon viewing this disclosure.

Teat models 128 comprise one or more models of a dairy livestock teatthat may be used to identify teat candidates or teats of a dairylivestock in a profile signal 134 or a 3D image 138. Examples of teatmodels 128 includes, but are not limited to, data sets, images,computer-aided design (CAD) models, 3D renderings, point clouds,two-dimensional (2D) renderings, geometric shape models, and boundaryrepresentations. In one example, a teat model 128 may be compared tofeatures of a dairy livestock in a 3D image 138 or a profile signal 134to identity teat candidates. In another example, a teat model 128 may becompared to one or more teat candidates to determine whether a teatcandidate is a teat. In another example, a teat model 128 may becompared to one or more edge pairs in a profile signal 134 to determinewhether an edge pair is a teat candidate. Examples of using a teat model128 to identify a teat candidate or teat is described in FIGS. 4, 6, 9,10, 13A, and 13B. In one embodiment, teat models 128 may be generatedbased on historical information associated with dairy livestock. Forinstance, a teat model 128 may be generated based on measurements of aparticular dairy livestock and may be specific to the dairy livestock.In another embodiment, teat models 128 may be generated based onstatistical or geometric information about dairy livestock teats and maynot be specific to a particular dairy livestock.

Tail models 129 comprise one or more models of a dairy livestock tailthat may be used to identify a dairy livestock tail in a 3D image 138.Examples of tail models 129 includes, but are not limited to, a dataset, images, CAD models, 3D renderings, point clouds, 2D renderings,geometric shape models, and boundary representations. In one embodiment,a tail model 129 may comprise a 2D geometric shape model or a 3Drendering of a predetermined tail shape that corresponds with a tail ofa dairy livestock. Examples of predetermined tail shapes include, butare not limited to, an ellipse and an ellipsoid. In one example, a tailmodel 129 may be compared to features of a dairy livestock in a 3D image138 or a profile signal 134 to identity tail candidates. In anotherexample, one or more tail candidates in a 3D image 138 may be comparedto a tail model 129 to determine whether tail candidates are a portionof the tail of the dairy livestock. Examples of using a tail model 129to identify portions of a dairy livestock tail are described in FIGS. 7,8, and 14A, and 14B. In one embodiment, tail models 129 may be generatedbased on historical information associated with dairy livestock. Forinstance, a tail model 129 may be generated based on measurements of aparticular dairy livestock and may be specific to the dairy livestock.In another embodiment, tail models 129 may be generated based onstatistical or geometric information about dairy livestock tails and maynot be specific to a particular dairy livestock.

Teat location information 130 and position maps 131 may compriseposition information associated with teat identifiers 127 and/orlocations for a plurality of teats for one or more dairy livestock.Position maps 131 may comprise graphical representations of informationderived from teat location information 130. For example, a position map131 may be a graph of the location of one or more teats of a dairylivestock. Examples of a position map 131 are described in FIGS. 11 and16. In one embodiment, teat location information 130 may be generatedbased on historical information associated with one or more dairylivestock. For instance, teat location information 130 may be generatedbased on measurements of the teat locations for a particular dairylivestock and may be specific to the dairy livestock. Examples of usingteat location information 130 are described in FIGS. 11 and 12. Inanother embodiment, teat location information 130 may be generated basedon statistical or geometric information about teat locations from dairylivestock and may not be specific to a particular dairy livestock.

The network interface 106 may comprise or may be integrated with amodem, a switch, a router, a bridge, a server, or a client. The networkinterface 106 may be configured to enable wired and/or wirelesscommunications and to communicate data through a network, system, and/ordomain. The processor 102 may be configured to send and to receive datausing network interface 106 from a network or a remote source. Forinstance, the processor 102 may be configured to send and receiveinformation about a dairy livestock using network interface 106.

Examples of the user interface 108 include, but are not limited to,touch screens, a light emitting diode (LED) display, an organic LED(OLED) display, an active matric OLED (AMOLED), a projector display, acathode ray (CRT) monitor, or any other suitable type of display aswould be appreciated by one of ordinary skill in the art upon viewingthis disclosure. The user interface 108 may be configured to presentinformation to a user using the vision system 100. For example, the userinterface 108 may comprise a graphical user interface (GUI). The GUI maybe employed to provide interfaces that allow the operator to view andinteract with programs or instructions executed on the vision system100.

The I/O interface 110 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata as would be appreciated by one of ordinary skill in the art uponviewing this disclosure. For example, the I/O interface 110 may beconfigured to communicate data between the processor 102 and peripheralhardware such as a mouse, a keyboard, or a touch sensor (e.g. a touchscreen).

In one embodiment, the vision devices 112 may comprise a laser 132 and a3D camera 136. For example, the visioning devices 112 may comprise alaser 132 configured to measure depth information and to generateprofile signals 134 of one or more surfaces of a dairy livestock.Additional information about profile signals 134 is described in FIG. 9.Examples of 3D cameras 136 include, but are not limited to,time-of-flight cameras. The visioning devices 112 may comprise a 3Dcamera 136 configured to generate depth maps and/or 3D images 138 of adairy livestock. Examples of 3D images 138 include, but are not limitedto, point clouds, depth maps, and range maps. Additional information andexamples of 3D images 138 are described in FIGS. 3-8. The visioningdevices 112 may further comprise other cameras (e.g. a color camera oran infrared camera) and/or any other imaging devices for capturingvisual information of a dairy livestock. Generally, the visioningdevices 112 may be configured to capture or to generate visualinformation from a dairy livestock.

The vision system 100 may be communicatively coupled to a robotic armsystem 140. The robotic arm system 140 comprises a robotic arm. Therobotic arm may be coupled to processor 102 and/or another processor orcontroller. The processor coupled to the robotic arm may be configuredto send instructions to move the robotic arm to a particular position ororientation. Also, the processor may be configured to send instructionsfor performing various operations such as attaching or removing teatcups to teats of a dairy livestock. Additional information and anexample of a robotic arm is described in FIG. 2.

FIG. 2 is a side view of an embodiment of a dairy livestock 202 and arobotic arm 200 employing the vision system 100. In one embodiment, therobotic arm 200 comprises a main arm 204, a supplemental arm 206, agripping portion 208, a laser 132, and a 3D camera 136. The robotic arm200, laser 132, and/or 3D camera 136 may be configured as shown or inany other suitable configuration. The laser 132 and the 3D camera 136may be positioned at any suitable location along the main arm 204 or thesupplemental arm 206. For example, the laser 132 may be coupled to thegripping portion 208 of the supplemental arm 206 at a location proximateto the part of the gripping portion 208 adapted to hold a teat cup 210and the 3D camera 136 may be coupled to the supplemental arm 206 at alocation between the laser 132 and the main arm 204.

The robotic arm 200 may be configured to employ the 3D camera 136 and/orthe laser 132 of the vision system 100 to identify features of the dairylivestock 202 which may allow the robotic arm 200 to perform variousoperations on the dairy livestock 202. Examples of dairy livestockfeatures include, but are not limited to, a dairy livestock tail 201,teats 203, and legs (e.g. hind legs) 205. In various embodiments, thedairy livestock 202 may have a plurality of teats 203. Generally, dairylivestock 202, such as cows, have four teats 203. Teats 203 may bepositioned in a predetermined orientation on an udder of the dairylivestock 202. For example, there may be a front right teat 203, a frontleft teat 203, a rear right teat 203, and a rear left teat 203, wherethe front teats 203 are closer to the head of the dairy livestock 202and the rear teats 203 are closer to the rear of the dairy livestock202.

As an example, the robotic arm 200 may be configured to retrieve a teatcup 210, such as teat preparation cup or a milking cup, to move the teatcup 210 toward a teat 203 of the dairy livestock 202 within a stall(e.g. a milking station or box), and to attach the teat cup 210 to theteat 203 of the dairy livestock 202. Teat cup 210 may be any suitablecontainer or conduit through which fluid may flow. For example, teat cup210 may comprise a flexible material which may compress and expand inresponse to internal and/or external air pressure changes. The teat cup210 may comprise multiple openings. For instance, the teat cup 210 maycomprise a first opening large enough for a teat 203 to be inserted intothe teat cup 203. The teat cup 210 may comprise a second opening whichmay serve as an ingress for the teat cup 210, for example, to allowtreatment fluids such as detergents and chemicals to flow into the teatcup 210. The teat cup 210 may comprise a third opening which may serveas an egress for the teat cup 210, for example, to allow fluids such asmilk, detergents, and chemicals to exit the teat cup 210.

In one embodiment, the robotic arm 200 may be configured to employ the3D camera 136 when the robotic arm 200 is at approximately a firstdistance 212 away from the dairy livestock 202 and may use the laser 132when the robotic arm 200 is closer to the dairy livestock 202 atapproximately a second distance 214 away from the dairy livestock 202.For instance, the robotic arm 200 may be configured to perform one ormore operations on the teats 203 of the dairy livestock 202. The roboticarm 200 may employ the 3D camera 136 at approximately the first distance212 away from the dairy livestock 202 to locate the rear hind legs 205of the dairy livestock 202. The robotic arm 200 may then use informationderived from the 3D camera 136 to position the robotic arm 200 atapproximately the second distance 214 away from the dairy livestock 202.For example, the robotic arm 200 may position the laser 132 to the rearof and below the dairy livestock 202 to locate the teats 203 of thedairy livestock 202. The robotic arm 200 may be further configured toemploy the laser 132 to locate one or more teats 203 of the dairylivestock 202. In other examples, the robotic arm 200 may use the 3Dcamera 136 and/or the laser 132 at any suitable distances away from thedairy livestock 202. The robotic arm 200 may also be configured to bepositioned at any suitable locations with respect to the dairy livestock202, for example, in front of the dairy livestock 202 or on the side ofthe dairy livestock 202.

FIG. 3 is a perspective view of an embodiment of a plurality of imagedepth planes 302 in a 3D image 138 of a dairy livestock 202. Image depthplanes 302 may be used to isolate and view specific portions of a dairylivestock 202 within the 3D image 138. The dairy livestock 202 isoriented within the 3D image 138 with respect to an x-axis 304, a y-axis306, and a z-axis 308. The x-axis 304 corresponds with a horizontaldimension of the 3D image 138. The y-axis 306 corresponds with avertical dimension of the 3D image 138. The z-axis 308 corresponds witha depth dimension into the 3D image 138.

Each image depth plane 302 is a two-dimensional view plane of the dairylivestock 202 that extends along the x-axis 304 and the y-axis 306.Image depth planes 302 may be used to partition the dairy livestock 202or a portion of the dairy livestock 202 along the z-axis 308 into aplurality of view planes or image slices. The dairy livestock 202 may bepartitioned into any suitable number and/or combination of image depthplanes 302. Image depth planes 302 may use any suitable spacing betweenthe image depth planes 302 along the z-axis 308. For example, theplurality of image depth planes 302 may be equidistant apart from eachother, or image depth planes 302 may be configured to partition theentire length of the dairy livestock 202 along the z-axis 308 or just aportion of the dairy livestock 202 along the z-axis 308.

Additional information and examples of using image depth planes 302within a 3D image 138 are described in FIGS. 4-8. The vision system 100may use 3D images 138 to identify features (e.g. a tail 201, teats 203,and/or legs 205) of the dairy livestock 202 and/or to identify potentialaccess regions where a robotic arm 200 can access teats 203 of the dairylivestock 202. FIGS. 4 and 7 are examples of 3D images 138 used by thevision system 100 to identify features of the dairy livestock 202. FIGS.5, 6, and 8 are embodiments of the vision system 100 using 3D images 138to identify features of the dairy livestock 202. The vision system 100may be configured to use the 3D image 138 to identify features of thedairy livestock 202 to facilitate performing one or more operations onthe dairy livestock 202. Examples of operations that are performed onthe dairy livestock 202 includes, but are not limited to, teatpreparation and milk extraction.

FIG. 4 is an embodiment of a 3D image 138 of a rearview of a dairylivestock 202 in a stall 402. In FIG. 4, the vision system 100 may usethe 3D image 138 to detect legs 205 and teats 203 of the dairy livestock202. Each pixel of the 3D image 138 may be associated with a depthvalue. In other words, the color, intensity, and/or numeric value ofeach pixel in the 3D image 138 may be associated with a particular depthwith respect to the z-axis 308. The vision system 100 may be configuredto use a depth value threshold 125 to identify one or more regions 410within the 3D image 138. A depth value threshold 125 may be representedby any a color, intensity, numeric value, or any other suitableindicator of a particular depth in the 3D image 138. For example, thevision system 100 may compare portions of the 3D image 138 to the depthvalue threshold 125 and to identify one or more regions 410 in the 3Dimage 138 that comprise depth values greater than a depth valuethreshold 125. A region 410 with a depth value greater than the depthvalue threshold 125 indicates that the region 410 extends to at least apredetermined depth along the z-axis 308 into the 3D image 138.Comparing the portions of the 3D image 138 to the depth value threshold125 allows the vision system 100 to identify one or more regions 410with a depth that is suitable to be considered a thigh gap region 412.

A thigh gap region 412 is a region 410 that corresponds with a space oropening between the hind legs 205 of the dairy livestock 202. The thighgap region 412 is a region 410 where the vision system 100 may define ordemarcate an access region 418 that provides a suitable amount ofclearance for a robotic arm 200 to access the teats 203 of the dairylivestock 202. The vision system 100 may apply one or more rules from athigh gap rule set 126 to the identified regions 410 in order todetermine whether a region 410 is the thigh gap region 412. Examples ofapplying rules from a thigh gap rule set 126 are described in FIG. 5. Inother embodiments, the thigh gap region 412 may correspond with a spaceor opening between any other pairs of legs 205 of the dairy livestock202.

The vision system 100 may be configured to demarcate an access region418 within the thigh gap region 412. An access region 418 may be an areaor space that provides a suitable amount of clearance for a robotic arm200 to access the teats 203 of the dairy livestock 202. The accessregion 418 may comprise a first vertical edge 420, a second verticaledge 422, a first lower edge 424 spanning between the first verticaledge 420 and the second vertical edge 422, and a first upper edge 426spanning between the first vertical edge 420 and the second verticaledge 422. The first vertical edge 420 and the second vertical edge 422of the access region 418 may generally define the width 428 of theaccess region 418. In other words, the width 428 of the access regions418 corresponds with a dimension (e.g. the x-axis 304) that spansbetween the hind legs 205 of the dairy livestock 202. The vision system100 may be configured to determine and/or output position informationfor the first vertical edge 420, the second vertical edge 422, the firstupper edge 426, and/or the first lower edge 424 of the access region418. For instance, the vision system 100 may determine and/or output theposition of the first vertical edge 420 and the second vertical edge 422based on their pixel locations in the 3D image 138, Cartesiancoordinates or vectors with respect to the x-axis 304, the y-axis 306,and the z-axis 308, or any other suitable technique as would beappreciated by one of ordinary skill in the art upon viewing thisdisclosure.

In one embodiment, the vision system 100 may be configured to reduce thewidth 428 of the access region 418, for example, to provide a safetymargin for when a robotic arm 200 positions itself adjacent to the dairylivestock 202. Reducing the width 428 of the access region 418 may helpthe robotic arm 200 to avoid the legs 205 of the dairy livestock 202.The vision system 100 may shift the first vertical edge 420 and/or thesecond vertical edge 422 of the access region 418 to reduce the width428 of the access region 418. For example, the vision system 100 mayshift the first vertical edge 420 of the access region 418 along thex-axis 304 toward the second vertical edge 422 of the access region 418by a first offset value 421. The vision system 100 may also shift thesecond vertical edge 422 of the access region 418 along the x-axis 304toward the first vertical edge 420 of the access region 418 by a secondoffset value 423. The first offset value 421 and the second offset value423 may be the same or different. The vision system 100 may beconfigured to determine and/or output position information for theshifted first vertical edge 420 and the shifted second vertical edge 422of the access region 418.

In one embodiment, the vision system 100 may be configured to establishboundaries for a robotic arm 200 to limit the movement of a robotic arm200 based on the access region 418. For example, the vision system 100may be configured to set a first boundary 425 at the location of thefirst vertical edge 420 and a second boundary 427 at the location of thesecond vertical edge 422. The movement of a robotic arm 200 may belimited to the space within the first boundary 425 and the secondboundary 427.

In one embodiment, the vision system 100 may be configured to demarcatea teat detection region 434. A teat detection region 434 may be an areathat the vision system 100 examines to detect and identify teatcandidates 442 and/or teats 203 of the dairy livestock 202. In general,the teat detection region 434 may be an area where teats 203 of thedairy livestock 202 are likely to be located.

The teat detection region 434 may comprise a third vertical edge 436extending vertically from the first vertical edge 420 of the accessregion 418, a fourth vertical edge 438 extending vertically from thesecond vertical edge 422 of the access region 418, a second upper edge442 spanning between the third vertical edge 436 and the fourth verticaledge 438, and a second lower edge 440 spanning between the thirdvertical edge 436 and the fourth vertical edge 438. The second loweredge 440 may be adjacent to or coincident with the first upper edge 426of the access region 418. The vision system 100 may be configured topartition the 3D image 138 within the teat detection region 434 alongthe z-axis 308 to generate a plurality of image depth planes 302 and toexamine each of the plurality of image depth planes 302 for teatcandidates 442 as explained below with respect to FIG. 6.

In one embodiment, the vision system 100 may be configured to generate aprofile signal 134 of at least a portion of the dairy livestock 202within an image depth plane 302. For instance, the vision system 100 maygenerate the profile signal 134 based on a profile or a surface of aportion of the dairy livestock 202 within an image depth plane 302, forexample, an udder of the dairy livestock 202. The vision system 100 maybe further configured to process the profile signal 134 to determineposition information for the one or more teat candidates 442. Examplesof the vision system 100 processing the profile signal 134 to determineposition information teat candidates 442 and/or teats 203 are describedin FIGS. 9 and 10.

In another embodiment, the vision system 100 may be configured toidentify one or more teat candidates 442 within an image depth plane 302and/or to apply one or more rules from a teat detection rule set 124 tothe one or more teat candidates 442 to identify one or more teats 203.Examples of applying rules from a teat detection rule set 124 to teatcandidates 442 are described in FIGS. 6, 13A, and 13B.

The vision system 100 may be configured to determine and/or outputposition information for the one or more teat candidates 442 and the oneor more teats 203. For instance, the vision system 100 may determineand/or output the position of the one or more teat candidates 442 andthe one or more teats 203 based on their pixel locations in the 3D image138, Cartesian coordinates or vectors with respect to the x-axis 304,the y-axis 306, and the z-axis 308, or any other suitable technique aswould be appreciated by one of ordinary skill in the art upon viewingthis disclosure.

FIG. 5 is a flowchart of an embodiment of a leg detection method 500using the vision system 100. In one embodiment, the vision system 100may employ method 500 to detect and to determine the position of legs205 of a dairy livestock 202. For example, the vision system 100 may beconfigured to determine the position of the hind legs 205 of the dairylivestock 202 in order to identify an access region 418 where a roboticarm 200 may safely approach the dairy livestock 202.

At step 502, the vision system 100 obtains a 3D image 138 of a rearviewof a dairy livestock 202 in a stall 402. In one embodiment, the visionsystem 100 may obtain the 3D image 138 by employing a 3D camera 136 togenerate the 3D image 138. In another embodiment, the vision system 100may obtain the 3D image 138 from a memory (e.g. memory 104). Forexample, the 3D image 138 may be previously captured and stored into amemory.

At step 504, the vision system 100 identifies one or more regions 410within the 3D image 138 comprising depth values greater than a depthvalue threshold 125. The one or more regions 410 may represent areas inthe 3D image 138 with enough depth with respect to the z-axis 308 topotentially be a thigh gap region 412. The vision system 100 may compareportions of the 3D image 138 to the depth value threshold 125 toidentify one or more regions 410 in the 3D image 138 that comprise depthvalues greater than the depth value threshold 125. A region 410 with adepth value greater than the depth value threshold 125 may indicate thatthe region 410 extends to at least a predetermined depth along thez-axis 308 into the 3D image 138.

At step 506, the vision system 100 applies one or more rules from athigh gap detection rule set 126 to the one or more regions 410 toidentify a thigh gap region 412 among the one or more regions 410. Thevision system 100 may apply the one or more rules from the thigh gapdetection rule set 126 to a region 410 from the one or more regions 410.Non-limiting examples of the vision system 100 applying one or morerules from the thigh gap detection rule set 126 to regions 410 aredescribed below. At step 508, the vision system 100 determines whetherthe region 410 satisfied the one or more rules of the thigh gapdetection rule set 126. The vision system 100 may proceed to step 510when the region 410 does not satisfy the one or more rules of the thighgap detection rule set 126. Otherwise, the vision system 100 may proceedto step 512 when the region 410 satisfies the one or more rules of thethigh gap detection rule set 126. At step 510, the vision system 100 maydiscard the region 410 from the one or more regions 410 in response todetermining that the region 410 does not satisfy the one or more rulesof the thigh gap detection rule set 126, and therefore is not the thighgap region 412. At step 512, the vision system 100 may identify theregion 410 as the thigh gap region 412 in response to determining thatthe region 410 satisfies the one or more rules of the thigh gapdetection rule set 126.

As an example, the thigh gap rule set 126 may identify a marker 414positioned between the hind legs 205 of the dairy livestock 202 adjacentto or coincident with a lower edge 416 of the 3D image 138. One or moremarkers 414 may be employed by the vision system 100 to indicate userdefined features or logic that may be used for identifying areas ofinterest (e.g. thigh gap region 412, teat detection region 434, and/ortail detection region 702). For example, a user may set a marker 414 ina location within a 3D image 138 where a thigh gap region 412 is morelikely to occur such as between the hind legs 205 of the dairy livestock202 and proximate to a lower edge of the 3D image 138. The vision system100 may be configured to employ markers 414 as part of a decisionprocess to discard regions 410 that do not comprise the marker 414 andidentify a region 410 as the thigh gap region 412 when the region 410comprises the marker 414. Markers 414 may be set at predefined locationsbefore processing the 3D image 138 to identify the thigh gap region 412or may be set while processing the 3D image 138 to identify the thighgap region 412.

As another example, the thigh gap rule set 126 may identify one or moreboundaries within the 3D image 138 and may discard regions 410 that areoutside of the defined boundaries, coincident with, or share an edgewith the defined boundaries. For example, the thigh gap rule set 126 mayidentify a first 3D image edge 430 and a second 3D image edge 432 andmay discard regions 410 that share an edge with either the first 3Dimage edge 430 or the second 3D image edge 432. Since the thigh gapregion 412 may be generally located between a pair of legs 205 of thedairy livestock 202 in a central portion of the 3D image 138, the visionsystem 100 may discard regions 410 that share an edge with either thefirst 3D image edge 430 or the second 3D image edge 432. The visionsystem 100 may also be configured to identify a region 410 as the thighgap region 412 when the region 410 does not share an edge with eitherthe first 3D image edge 430 or the second 3D image edge 432.

As another example, the thigh gap rule set 126 may comprise a rule thatindicates a minimum or a maximum area value with respect to the x-axis304 and the y-axis 306 to be considered a thigh gap region 412. Theminimum or maximum area value may be determined and set to provideenough clearance for a robotic arm 200. For instance, the vision system100 may be configured to compare one or more regions 410 to a minimumarea value to be considered a thigh gap region 412 and may discardregions 410 with an area less than the minimum area value to beconsidered the thigh gap region 412. In other words, the vision system100 may discard or reject regions 410 that are too small to be the thighgap region 412. The vision system 100 may be configured to identify aregion 410 as the thigh gap region 412 when the region 410 has an areagreater than or equal to the minimum area value to be considered thethigh gap region 412. As another example, the vision system 100 may beconfigured to compare one or more regions 410 to a maximum area value tobe considered a thigh gap region 412 and may discard regions 410 with anarea greater than the maximum area value to be considered the thigh gapregion 412. In other words, the vision system 100 may discard or rejectregions 410 that are too large to be the thigh gap region 412. Thevision system 100 may be configured to identify a region 410 as thethigh gap region 412 when the region 410 has an area less than or equalto the maximum area value to be considered the thigh gap region 412. Ingeneral, the vision system 100 may apply such rules to reject or ignoreregions 410 that are either too small to provide enough clearance forthe robotic arm 200, and therefore, are not the thigh gap 412 or regions410 that are too large to be a space between the legs 205 of the dairylivestock 202.

As another example, the thigh gap rule set 126 may comprise a rule thatindicates a minimum or maximum height value with respect to the y-axis306 to be considered a thigh gap region 412. The minimum or maximumheight value may be determined and set to provide enough clearance for arobotic arm 200. For example, the vision system 100 may be configured tocompare the height of one or more regions 410 to a minimum height valueto be considered a thigh gap region 410 and may discard regions 410 witha height less than the minimum height value to be considered the thighgap region 412. In other words, the vision system 100 may discard orreject regions 410 that are too short to be the thigh gap region 412.The vision system 100 may be configured to identify a region 410 as thethigh gap region 412 when the region 410 has a height greater than orequal to the minimum height value to be considered the thigh gap region412. As another example, the vision system 100 may be configured tocompare the height of one or more regions 410 to a maximum height valueto be considered a thigh gap region 410 and may discard regions 410 witha height greater than the maximum height value to be considered thethigh gap region 412. In other words, the vision system 100 may discardor reject regions 410 that are too tall to be the thigh gap region 412.The vision system 100 may also be configured to identify a region 410 asthe thigh gap region 412 when the region 410 has a height less than orequal to the maximum height value to be considered the thigh gap region412. In general, the vision system 100 may apply such rules to reject orignore regions 410 with a vertical dimension with respect to the y-axis306 is too short to provide clearance for the robotic arm 200, andtherefore, are not the thigh gap region 412 or too tall to be spacebetween the legs 205 of the dairy livestock 202.

As another example, the thigh gap rule set 126 may comprise a rule thatindicates a minimum or maximum width value with respect to the x-axis304 to be considered a thigh gap region 412. The minimum or maximumwidth value may be determined and set to provide enough clearance for arobotic arm 200. For example, the vision system 100 may be configured tocompare the width of one or more regions 410 to a minimum width value tobe considered a thigh gap region 410 and may discard regions 410 with awidth less than the minimum width value to be considered the thigh gapregion 412. In other words, the vision system 100 may discard or rejectregions 410 that are too narrow to be the thigh gap region 412. Thevision system 100 may also be configured to identify a region 410 as thethigh gap region 412 when the region 410 has a width greater than orequal to the minimum width value to be considered the thigh gap region412. As another example, the vision system 100 may be configured tocompare the width of one or more regions 410 to a maximum width value tobe considered a thigh gap region 410 and may discard regions 410 with awidth greater than the maximum width value to be considered the thighgap region 412. In other words, the vision system 100 may discard orreject regions 410 that are too wide to be the thigh gap region 412. Thevision system 100 may also be configured to identify a region 410 as thethigh gap region 412 when the region 410 has a width less than or equalto the maximum width value to be considered the thigh gap region 412. Ingeneral, the vision system 100 may apply such rules to reject or ignoreregions 410 with a horizontal dimension with respect to the x-axis 304is too narrow to provide clearance for the robotic arm 200, andtherefore, are not the thigh gap region 412 or too wide to be a spacebetween the legs 205 of the dairy livestock 202.

At step 514, the vision system 100 determines whether the thigh gapregion 412 has been identified from among the one or more regions 410.The vision system 100 returns to step 506 in response to determiningthat the thigh gap region 412 has not been identified. The vision system100 may proceed to step 516 in response to determining that the thighgap region 412 has been identified from among the one or more regions410. For example, the vision system 100 may proceed to step 516 when thethigh gap region 412 has been identified even if there are still moreregions 410 from the one or more regions 410 to examine.

At step 516, the vision system 100 demarcates an access region 418within the thigh gap region 412. In one embodiment, the vision system100 may demarcate the access region 418 by determining the largestregion (e.g. rectangular region) that fits within the thigh gap region412. For example, the vision system 100 may set the first vertical edge420 of the access region 418 at one of the edges of the thigh gap region412 that is adjacent to one of the legs 205 of the dairy livestock 202and the second vertical edge 220 of the access region 418 at anotheredge of the thigh gap region that corresponds with another leg 205 ofthe dairy livestock 202. The vision system 100 may then set the firstlower edge 424 and the first upper edge 426 within the thigh gap region412. In other embodiments, the vision system 100 may demarcate theaccess region 418 by determining a region within the thigh gap region418 that accommodates a predetermined access region 418 shape or area.

At step 518, the vision system 100 determines whether to reduce thewidth 428 of the access region 418. For example, the vision system 100may be configured to provide a safety margin for avoiding contact withthe legs 205 of the dairy livestock 202 and to reduce the width 428 ofthe access region 418. The vision system 100 may proceed to step 520 inresponse to determining to reduce the width 428 of the access region410. Otherwise, the vision system 100 may proceed to step 522 inresponse to determining not to reduce the width 428 of the access region410. The vision system 100 may determine whether to reduce the width 428of the access region 418 and the amount of width reduction based on userinput and/or predefined instructions.

At step 520, the vision system 100 reduces the width 428 of the accessregion 418 by shifting the first vertical edge 420 and the secondvertical edge 422 of the access region 418. As an example, the visionsystem 100 may shift the first vertical edge 420 toward the secondvertical edge 422 and may shift the second vertical edge 422 toward thefirst vertical edge 420. Reducing the width 428 of the access region 418may further restrict the movement of the robotic arm 200 with respect tothe x-axis 304 to avoid contacting the legs 205 of the dairy livestock202.

At step 522, the vision system 100 determines position information forthe first vertical edge 420 and the second vertical edge 422 of theaccess region 418. The vision system 100 may determine the position ofthe first vertical edge 420 and the second vertical edge 422 based ontheir pixel locations in the 3D image 138, Cartesian coordinates orvectors with respect to the x-axis 304, the y-axis 306, and the z-axis308, or any other suitable technique as would be appreciated by one ofordinary skill in the art upon viewing this disclosure. In otherembodiments, the vision system 100 may determine position informationfor the first upper edge 426 and/or the first lower edge 424. The visionsystem 100 may also output the position information for the firstvertical edge 420, the second vertical edge 422, the first upper edge426, and/or the first lower edge 424.

FIG. 6 is a flowchart of an embodiment of a teat detection method 600using the vision system 100 with a 3D image 138. In one embodiment, thevision system 100 may employ method 600 to detect and to determine theposition of one or more teat candidates 442 or one or more teats 203 ofa dairy livestock 202. For example, the vision system 100 may employmethod 600 to determine the position of the one or more teats 203 inorder to perform operations (e.g. teat preparation and/or milkextraction) on the dairy livestock 202.

At step 602, the vision system 100 obtains a 3D image 138 of a rearviewof a dairy livestock 202 in a stall 402. The vision system 100 mayobtain the 3D image 138 of the dairy livestock 202 similarly to asdescribed in step 502 of FIG. 5.

At step 604, the vision system 100 identifies one or more regions 410within the 3D image 138 comprising depth values greater than a depthvalue threshold 125. The vision system 100 may identify the one or moreregions 410 within the 3D image 138 similarly to as described in step504 of FIG. 5.

At step 606, the vision system 100 applies one or more rules from athigh gap detection rule set 126 to identify a thigh gap region 412among the one or more regions 410. The vision system 100 may apply oneor more rules from a thigh gap detection rule set 126 to identify thethigh gap region 412 similarly to as described in steps 506-514 of FIG.5.

At step 608, the vision system 100 demarcates an access region 418within the thigh gap region 412. The vision system 100 may demarcate theaccess region 418 within the thigh gap region 412 similarly to asdescribed in step 516 of FIG. 5. In one embodiment, the vision system100 may also reduce the width 428 of the access region 418 similarly toas described in step 520 of FIG. 5.

At step 610, the vision system 100 demarcates a teat detection region434 within the 3D image 138. In one embodiment, the vision system 100may set the third vertical edge 436 of the teat detection region 434 toextend vertically from the first vertical edge 420 of the access region418 and the fourth vertical edge 438 of the teat detection region 434 toextend vertically from the second vertical edge 422 of the access region418. The vision system 100 may then set the second lower edge 440 andthe second upper edge 442 of the teat detection region 434. In oneembodiment, the second lower edge 440 is adjacent to or coincident withthe first upper edge 426 of the access region 418 and the second upperedge 442 is adjacent to or coincident with an upper edge of the 3D image138.

At step 612, the vision system 100 partitions the 3D image 138 withinthe teat detection region 434 along the z-axis 308 to generate aplurality of image depth planes 302. The vision system 100 may partitionthe 3D image 138 along the z-axis 308 into any suitable number of imagedepth planes 302 with any suitable spacing between the image depthplanes 302.

At step 614, the vision system 100 identifies one or more teatcandidates 442 within an image depth plane 302 from the plurality ofimage depth planes 302. The vision system 100 may compare a teat model128 to features of the dairy livestock 202 in the image depth plane 302to identify teat candidates 442 within the image depth plane 302.

The vision system 100 may iteratively examine each image depth planes302 from the plurality of image depth planes 302 to identify features ofthe dairy livestock 202 and then compare the identified features to theteat model 128. The vision system 100 may progressively advance throughthe image depth planes 302 along the z-axis 308 in a direction wheredepth increases. During each iteration, the vision system 100 advancesthrough the image depth planes 302 and may identify one or morefeatures. Some features of the dairy livestock 202 may correspond withdesired features (e.g. teats 203) of the dairy livestock 202, but thesefeatures may not be identifiable as teat candidates 442 until anappropriate depth is reached in the plurality of image depth planes 302.For example, an initial image depth plane 302 may comprise a featurethat represents a portion of a teat 203, however, the teat 203 may notbe identifiable as a teat candidate 442 until subsequent image depthplanes 203 where features of the teat 203 are more pronounced. Examplesof examining features of a dairy livestock 202 in an image depth plane302 are described in FIGS. 13A and 13B.

In one embodiment, the teat model 128 may comprise a teat shape (e.g. anellipse) and the vision system 100 may compare the teat shape tofeatures of the dairy livestock 202 within the image depth plane 302.The vision system 100 may be configured to iterative compare propertiesof the teat model 128, such as geometric shape and size, to features ofthe dairy livestock 202 within one or more image depth planes 302. Forexample, the vision system 100 may compare the teat model 128 to one ormore features of the dairy livestock 202 in a first image depth 302 andmay discard or ignore features of the dairy livestock 202 that do notsubstantially match the teat model 128. The vision system 100 maycontinue to compare the teat model 128 to features of the dairylivestock 202 in one or more additional image depth planes 302. Thevision system 100 may identify features of the dairy livestock 202 asteat candidates 442 in response to determining that the features of thedairy livestock 202 correspond with the teat model 128 within one ormore image depth planes 302. Examples of comparing a teat model 128 tofeatures of the dairy livestock 202 are described in FIGS. 13A and 13B.

At step 616, the vision system 100 applies one or more rules from a teatdetection rule set 124 to each of the one or more teat candidates 442 toidentify one or more teats 203 from among the teat candidates 442. Theteat detection rule set 124 may comprise one or more rules that definecriteria for a teat candidate 442 to be considered a teat 203 of thedairy livestock 202. The vision system 100 may apply the one or morerules of the teat detection rule set 124 to each of the teat candidates442 to determine whether each of the teat candidates 442 meet thecriteria to be considered a teat 203. Non-limiting examples of thevision system 100 applying one or more rules from the teat detectionrule set 124 to teat candidates 442 are described below.

At step 618, the vision system 100 may determine whether the teatcandidate 442 satisfies the one or more rules of the teat detection ruleset 124. The vision system 100 may proceed to step 620 when the teatcandidate 442 does not satisfy the rules of the teat detection rule set124. In other words, the vision system 100 may proceed to step 620 whenthe teat candidate 442 does not meet the criteria to be considered ateat 203 of the dairy livestock 202. The vision system 100 may proceedto step 622 when the teat candidate 442 when the teat candidate 442satisfies the rules of the teat detection rule set 124. In other words,the vision system may proceed to step 622 when the teat candidate 442meets the criteria to be considered a teat 203 of the dairy livestock202. At step 620, the vision system 100 may discard or reject a teatcandidate 442 in response to determining that the teat candidate 442does not satisfy the one or more rules from the teat detection rule set124. At step 622, the vision system 100 may identify a teat candidate442 as a teat 203 of the dairy livestock 202 in response to determiningthat the teat candidate 442 satisfies the one or more rules from theteat detection rule set 124.

As an example, the teat detection rule set 124 may comprise a rule thatindicates a minimum or maximum area value with respect to the x-axis 304and the y-axis 306 to be considered a teat 203. The vision system 100may compare the area of each of the teat candidates 442 to a minimumarea value to be considered a teat 203 and may discard teat candidates442 with an area less than the minimum area value to be considered ateat 203. In other words, the vision system 100 may discard or rejectionteat candidates 442 that are too small to be a teat 203. The visionsystem 100 may identify a teat candidate 442 as a teat 203 when the teatcandidate 442 has an area greater than or equal to the minimum areavalue to be considered a teat 203. As another example, the vision system100 may compare the area of each of the teat candidates 442 to a maximumarea value to be considered a teat 203 and may discard teat candidates442 with an area greater than the maximum area value to be considered ateat 203. In other words, the vision system 100 may discard or rejectionteat candidates 442 that are too large to be a teat 203. The visionsystem 100 may identify a teat candidate 442 as a teat 203 when the teatcandidate 442 has an area less than or equal to the maximum area valueto be considered a teat 203. In general, the vision system 100 may applysuch rules to reject or ignore teat candidates 442 generated by featuresthat have area with respect to the x-axis 304 and the y-axis 306 that istoo small, for example, from a skin fold, or too large, for example,from an udder, to be a teat 203.

As another example, the teat detection rule set 124 may comprise a rulethat indicates a minimum or maximum height position value with respectto the y-axis 306 to be considered a teat 203. The minimum and maximumheight position value may determined and set to define limits fordiscarding and/or identifying teats 203 from among the teat candidates442. The vision system 100 may compare the height position of teatcandidates 442 with respect to the y-axis 306 to a minimum heightposition value to be considered a teat 203 and may discard teatcandidates 442 with a height position less than the minimum heightposition value to be considered a teat 203. In other words, the visionsystem 100 may discard or reject teat candidates 442 that are too low tobe a teat 203. The vision system 100 may identify a teat candidate 442as a teat 203 when the teat candidate 442 has a height position greaterthan or equal to the minimum height position value to be considered ateat 203. As another example, the vision system 100 may compare theheight position of teat candidates 442 with respect to the y-axis 306 toa maximum height position value to be considered a teat 203 and maydiscard teat candidates 442 with a height position greater than themaximum height position value to be considered a teat 203. In otherwords, the vision system 100 may discard or reject teat candidates 442that are too high to be a teat 203. The vision system 100 may identify ateat candidate 442 as a teat 203 when the teat candidate 442 has aheight position less than or equal to the maximum height position valueto be considered a teat 203. In general, the vision system 100 may applysuch rules to reject or ignore teat candidate 442 that are positionedtoo high or too low with respect to the y-axis 306 to be a teat 203. Inone embodiment, teats 203 may be generally located in a central portionof the 3D image 138, and therefore, teat candidates 442 that are closerto upper edge or a lower edge of the 3D image 138 are unlikely to beteats 203 of the dairy livestock 202.

As another example, the teat detection rule set 124 may comprise a rulethat indicates a minimum or maximum width value with respect to thex-axis 304 to be considered a teat 203. The vision system 100 maycompare the width of teat candidates 442 to a minimum width value to beconsidered a teat 203 and may discard teat candidates 442 with a widthless than the minimum width value to be considered a teat 203. In otherwords, the vision system 100 may discard or reject teat candidates 442that are too narrow to be a teat 203. The vision system 100 may identifya teat candidate 442 as a teat 203 when the teat candidate 442 has awidth greater than or equal to the minimum width value to be considereda teat 203. As another example, the vision system 100 may compare thewidth of teat candidates 442 to a maximum width value to be considered ateat 203 and may discard teat candidates 442 with a width greater thanthe maximum width value to be considered a teat 203. In other words, thevision system 100 may discard or reject teat candidates 442 that are toowide to be a teat 203. The vision system 100 may identify a teatcandidate 442 as a teat 203 when the teat candidate 442 has a width lessthan or equal to the maximum width value to be considered a teat 203. Ingeneral, the vision system 100 may apply such rules to reject or ignoreteat candidates 442 generated by features that have a width with respectto the x-axis 304 that is too narrow, for example, from a skin fold, ortoo large, for example, from an udder, to be a teat 203.

At step 624, the vision system 100 determines whether a target number ofteats 203 have been identified. The target number of teats 203 may be apredefined value set by an operator. For example, the vision system 100may be configured to identify four teats 203 of the dairy livestock 202.In other examples, the vision system 100 may be configured to find anyother suitable number of teats 203. The vision system 100 may return tostep 614 in response to determining that the target number of teats 203has not been identified. In other words, the vision system 100 maycontinue to search for and identify additional teats 203 until thetarget number of teats 203 has been identified. In one embodiment, thevision system 100 may adjust one or more properties of the teat model128 to identify other potential teat candidates 442. For example, thevision system 100 may adjust the size or shape from the teat model 128that used for comparisons to identify teat candidates 442. The visionsystem 100 may proceed to step 620 in response to determining that thetarget number of teats 203 has been identified.

At step 626, the vision system 100 determines position information forthe one or more teats 203. In one embodiment, the vision system 100 maydetermine the position for the one or more teats 203 based on theirrespective positions with respect to the plurality of image depth planes302. For example, the image depth plane 302 where a teat 203 isidentified may indicate a depth of the teat 203 with respect to thez-axis 308. The position of the teat 203 within the image depth plane302 may indicate the vertical position of the teat 203 with respect tothe y-axis 306 and the horizontal position of the teat 203 with respectto the x-axis 304. The vision system 100 may determine and/or output theposition information for the one or more teat candidates 442 and/or theone or more teats 203 based on their pixel locations in the 3D image138, Cartesian coordinates or vectors with respect to the x-axis 304,the y-axis 306, and the z-axis 308, or any other suitable technique aswould be appreciated by one of ordinary skill in the art upon viewingthis disclosure.

FIG. 7 is another embodiment of a 3D image 138 of a rearview of a dairylivestock 202 in a stall 402. FIG. 7 illustrates using a 3D image 138 todetect a tail 201 of the dairy livestock 202. Similar to FIG. 4, thedairy livestock 202 is oriented within the 3D image with respect to anx-axis 304, a y-axis 306, and a z-axis 308. The vision system 100 may beconfigured to identify a thigh gap region 412 and to demarcate an accessregion 418 similarly to as previously described in FIG. 4.

In one embodiment, the vision system 100 may be configured to demarcatea tail detection region 702. A tail detection region 702 may be an areathat the vision system 100 examines to detect and to identify tailcandidates 712 and/or a tail 201 of the dairy livestock 202. In general,the tail detection region 702 may be an area where the tail 201 of thedairy livestock 202 is likely to be located.

The tail detection region 702 may comprise a third vertical edge 704extending vertically from the first vertical edge 420 of the accessregion 418, a fourth vertical edge 706 extending vertically from thesecond vertical edge 422 of the access region 418, a second upper edge708 spanning between the third vertical edge 704 and the fourth verticaledge 706, and a second lower edge 710 spanning between the thirdvertical edge 704 and the fourth vertical edge 706. The second loweredge 710 may be adjacent to or coincident with the first upper edge 426of the access region 418.

The vision system 100 may be configured to partition the 3D image 138within the tail detection region 702 along the z-axis 308 to generate aplurality of image depth planes 302 and to examine each of the imagedepth planes 302 for tail candidates 712. The vision system 100 may beconfigured to identify one or more tail candidates 712 within an imagedepth plane 302 and to compare a tail model 129 to the one or more tailcandidates 712 to identify a tail 201 of the dairy livestock 202. In oneembodiment, the tail model 129 may indicate a predetermined tail shapeand the vision system 100 may be configured to compare the predeterminedtail shape to the one or more tail candidates 712. The vision system 100may be configured to discard tail candidates 712 that do not correspondwith the tail model 129 and to identify tail candidates 712 thatcorrespond with the tail model 129 as the tail 201 of the dairylivestock 202. In one embodiment, the vision system 100 may beconfigured to identify a tail candidate 712 as the tail 201 of the dairylivestock 202 in response to determining that the tail candidate 712corresponds with the tail model 129 for one or more image depth planes302. Examples of comparing a tail model 129 to tail candidates 712 aredescribed in FIGS. 14A and 14B.

The vision system 100 may be configured to determine and/or outputposition information for the one or more tail candidates 712 and/or thetail 201 of the dairy livestock 202. For instance, the vision system 100may determine and/or output the position of the one or more tailcandidates 712 and the tail 201 of the dairy livestock 202 based ontheir pixel location in the 3D image 138, Cartesian coordinates orvectors with respect to the x-axis 304, the y-axis 306, and the z-axis308, or any other suitable technique as would be appreciated by one ofordinary skill upon viewing this disclosure.

FIG. 8 is a flowchart of an embodiment of a tail detection method 800using the vision system 100. In one embodiment, the vision system 100may employ method 800 to detect and to determine the position of a tail201 of a dairy livestock 202. For example, the vision system 100 may beconfigured to determine the position of the tail 201 of the dairylivestock 202 to avoid the tail 201 while positioning a robotic arm 200and/or performing operations using the robotic arm 200.

At step 802, the vision system 100 obtains a 3D image 138 of a rearviewof a dairy livestock 202 in a stall 402. The vision system 100 mayobtain the 3D image 138 similarly to as described in step 502 of FIG. 5.

At step 804, the vision system 100 identifies one or more regions 410within the 3D image 138 comprising depth values greater than a depthvalue threshold 125. The vision system 100 may identify one or moreregions 410 within the 3D image 138 similarly to as described in step504 of FIG. 5.

At step 806, the vision system 100 applies one or more rules from athigh gap detection rule set 126 to the one or more regions 410 toidentify a thigh gap region 412 among the one or more regions 410. Thevision system 100 may be configured to apply one or more rules from athigh gap detection rule set 126 to a identify the thigh gap region 412similarly to as described in step 506-514 of FIG. 5.

At step 808, the vision system 100 demarcates an access region 418within the thigh gap region 412. The vision system 100 may demarcate theaccess region 418 similarly to as described in step 516 of FIG. 5. Inone embodiment, the vision system 100 may also reduce the width 428 ofthe access region 418 similarly to as described in step 520 of FIG. 5.

At step 810, the vision system 100 demarcates a tail detection region702 within the 3D image 138. In one embodiment, the vision system 100may set the third vertical edge 704 of the tail detection region 702 toextend vertically from the first vertical edge 420 of the access region418 and the fourth vertical edge 706 of the tail detection region 702 toextend vertically from the second vertical edge 422 of the access region418. The vision system 100 may then set the second upper edge 708 andthe second lower edge 710 of the tail detection region 702. In oneembodiment, the second lower edge 710 is adjacent to or coincident withthe first upper edge 426 of the access region 418 and the second upperedge 708 is adjacent to or coincident with an upper edge of the 3D image138.

At step 812, the vision system 100 partitions the 3D image 138 withinthe tail detection region 702 along the z-axis 308 to generate aplurality of image depth planes 302. The vision system 100 may partitionthe 3D image 138 along the z-axis 308 into any suitable number of imagedepth planes 302 with any suitable spacing between the image depthplanes 302.

At step 814, the vision system 100 identifies one or more tailcandidates 712 within an image depth plane 302 from the plurality ofimage depth planes 302. The vision system 100 may identify features ofthe dairy livestock 202 within one or more of the plurality of imagedepth planes 302 as tail candidates 712. In one embodiment, the visionsystem 100 may use one or more geometric shapes (e.g. an ellipse) toidentify features that correspond with tail candidates 712 within eachof the image depth planes 302. The one or more geometric shapes maycorrespond with tail shapes for dairy livestock 202. The vision system100 may identify a feature of the dairy livestock 202 as a tailcandidate 712 when the feature substantially matches one of thegeometric shapes that corresponds with a tail shape. At step 816, thevision system 100 compares each of the one or more tail candidates 712to a tail model 129 (e.g. a predetermined tail shape) to identify a tail201 of the dairy livestock 202 within the plurality of image depthplanes 302. Examples of comparing a tail model 129 to tail candidates712 are described in FIGS. 14A and 14B.

As an example, the vision system 100 may iteratively examine each of theimage depth planes 302 from the plurality of image depth plane 302 toidentify features of the dairy livestock 202 as tail candidates 712. Thevision system 100 may progressively advance through the image depthplanes 302 along the z-axis 308 in a direction where depth increases.During each iteration of the vision system 100 advancing through theimage depth plane 302, one or more tail candidates 712 may beidentified. The vision system 100 may then compare the identified tailcandidates 712 to the tail model 129 to identify a tail 201 of the dairylivestock 202. Some tail candidates 712 may correspond with the tail 201of the dairy livestock 202, but these tail candidates 712 may not beidentifiable as the tail 201 until an appropriate depth is reached inthe plurality of image depth planes 302. For example, an initial imagedepth plane 302 may comprise a tail candidate 712 that represents aportion of the tail 201, however, the tail candidate 712 may not beidentifiable as the tail 201 until subsequent image depth planes 302where the features of the tail 201 are more pronounced.

In one embodiment, the tail model 129 may comprise a tail shape and thevision system 100 may compare the tail shape to the tail candidates 712within each of the plurality of image depth planes 302. The visionsystem 100 may be configured to iteratively compare properties of thetail model 129, such as geometric shape and size, to the tail candidates712 within one or more of the image depth planes 302. For example, thevision system 100 may compare the tail model 129 to each of the tailcandidates 712 and may discard or ignore tail candidates 712 that do notsubstantially match the tail model 129. For example, the vision system100 may determine that the tail candidate 712 and the tail model 129 donot correspond with each other when the size and/or shape of the tailcandidate 712 does not match the tail model 129. The vision system 100may continue to compare the tail model 129 to tail candidates 712 in oneor more additional image depth planes 302. The vision system 100 mayidentify a tail candidate 712 as the tail 201 in response to determiningthat the tail candidate 712 corresponds with the tail model 129 withinone or more image depth planes 302. For example, the vision system 100may determine that the tail candidate 712 and the tail model 129correspond with each other when the size and/or shape of the tailcandidate 712 substantially matches the tail model 129. Examples ofcomparing a tail model 129 to tail candidates 712 are described in FIGS.14A and 14B.

At step 818, the vision system 100 determines whether the one or moretail candidates 712 correspond with the tail model 129. The visionsystem 100 may proceed to step 820 in response to determining that theone or more tail candidates 712 do not correspond with the tail model129. Otherwise, the vision system 100 may proceed to step 822 inresponse to determining that the one or more tail candidates correspondwith the tail model 129. At step 820, the vision system 100 discardstail candidates 712 that do not correspond with the tail model 129 andthen proceeds to step 824. The vision system 100 may discard ordisregard tail candidates 712 that are substantially different from thetail model 129 and are unlikely the tail 201 of the dairy livestock 202.At step 822, the vision system 100 identifies tail candidates 712 fromamong the one or more tail candidates 712 as a tail 201 of the dairylivestock 202 and then proceeds to step 824. The vision system 100identifies tail candidates 712 that match the tail model 129 and arelikely portions of the tail 201 of the dairy livestock 202.

At step 824, the vision system 100 determines whether the tail 201 ofthe dairy livestock 202 has been identified from among the one or moretail candidates 712. The vision system 100 may return to step 814 inresponse to determining that the tail 201 has not been identified. Inother words, the vision system 100 may continue to search for andidentify tail candidates 712 until the tail 201 has been identified. Inone embodiment, the vision system 100 may adjust one or more propertiesused to identify other tail candidates 712. For example, the visionsystem 100 may adjust the size or shape of a tail shape that is used toidentify tail candidates 712. The vision system 100 may proceed to step826 in response to determining that the tail 201 has been identified.

In another embodiment, the vision system 100 may proceed to step 826 inresponse to determining that the tail 201 has been identified in atleast two of the image depth planes 302 and may otherwise return to step814.

At step 826, the vision system 100 determines position information forthe tail 201 of the dairy livestock 202. In one embodiment, the visionsystem 100 may determine the position of the tail 201 based on itsposition with respect to the plurality of image depth planes 302. Forexample, the image depth plane 302 where the tail 201 is identified mayindicate a depth of the tail 201 with respect to the z-axis 308. Theposition of the tail 201 within the image depth plane 302 may indicatethe vertical position of the tail 201 with respect to the y-axis 306 andthe horizontal position of the tail 201 with respect to the x-axis 304.The vision system 100 may determine and/or output the position of thetail 201 of the dairy livestock 202 based on pixel locations in the 3Dimage 138, Cartesian coordinates or vectors with respect to the x-axis304, the y-axis 306, and the z-axis 308, or any other suitable techniqueas would be appreciated by one of ordinary skill in the art upon viewingthis disclosure.

FIG. 9 is an embodiment of a profile signal 134 used by the visionsystem 100 to identify features (e.g. teats 203) of the dairy livestock202. The vision system 100 may be configured to use the profile signal134 to identify features of the dairy livestock 202 to facilitateperforming one or more operations on the dairy livestock 202.

In one embodiment, the vision system 100 may be configured to useprofile signals 134 after repositioning of the robotic arm 200 adjacentto the dairy livestock 202 based on position information derived fromone or more 3D images 138. For example, the robotic arm 200 may movefrom approximately a first distance 212 away from the dairy livestock202 to approximately a second distance 214 away from the dairy livestock202 via an access region 418.

FIG. 9 is a graph 900 of an embodiment of a profile signal 134 of aportion of a dairy livestock 202. In one embodiment, the vision system100 is configured to employ the laser 132 to generate a profile signal134 of a portion of a dairy livestock 202. The profile signal 134 may beused by the vision system 100 to identify one or more teat candidates442 and/or teats 203 of the dairy livestock 202. An example of thevision system 100 using a profile signal 134 to identify one or moreteat candidates 442 is described in FIG. 10.

Axis 904 indicates distance or offset, for example in millimeters (mm),with respect to an first axis (e.g. the x-axis 304 with respect to therobotic arm 200 and the laser 132) and axis 906 indicates a distance oroffset, for example in mm, with respect to a second axis (e.g. thez-axis 308 with respect to the robotic arm 200 and the laser 132).Profile signals 134 comprises information associated with the relativedistance between the dairy livestock 202 and the robotic arm 200 alongthe x-axis 304. As an example, the profile signal 134 may be generatedby a laser 132 positioned at a point 902 located at 0 mm on the z-axis308 and 0 mm on the x-axis 304. In FIG. 9, negative values of the x-axis304 indicate positions to the left of the laser 132 and positive valuesof the x-axis 304 indicate positions to the right of the laser 132. Theprofile signal 134 comprises relative distance information from about200 mm to the left of the laser 132 (shown as −200 mm) to about 200 mmto the right of the laser 132.

At about −200 mm, the dairy livestock 202 may be about 450 mm from thelaser 132. From about −200 mm to about −125 mm, the distance between thelaser 132 and the dairy livestock 202 decreases from about 450 mm toabout 275 mm. The decrease in distance between the laser 132 and thedairy livestock 202 may indicate that the portion of the dairy livestock202 between about −200 mm and about −125 mm is extending towards thelaser 132. From about −125 mm to about −80 mm, the distance between thelaser 132 and the dairy livestock 202 remains relatively constant atabout 275 mm. From about −80 m to about −40 mm, the distance between thelaser 132 and the dairy livestock 202 increases from about 275 mm toabout 400 mm. The increase in distance between the laser 132 and thedairy livestock 202 may indicate that the portion of the dairy livestock202 between about −80 mm and about −40 mm is extending away from thelaser 132. Generally, the portion of the profile signal 134 from about−200 mm to about −40 mm represents a portion of the dairy livestock 202that extends toward the laser 132, has a relatively constant depthsection, and then extends away from the laser 132. The vision system 100may be configured to interpret this general pattern in the portion ofthe profile signal 134 from about −200 mm to about −40 mm as potentiallycomprising a teat 203.

From about −40 mm to about 10 mm along the x-axis 940, the distancebetween the laser 132 and the dairy livestock 202 decreases from about400 mm to about 250 mm. The decrease in distance between the laser 132and the dairy livestock 202 may indicate that the portion of the dairylivestock 202 between about −40 mm and about 10 mm along the x-axis 904is extending towards the laser 132. From about 10 mm to about 40 mmalong the x-axis 904, the distance between the laser 132 and the dairylivestock 202 remains relatively constant at about 250 mm. From about 40mm to about 200 mm along the x-axis 904, the distance between the laser132 and the dairy livestock 202 generally increases from about 250 mm toabout 450 mm. The increase in distance between the laser 132 and thedairy livestock 202 may indicate that the portion of the dairy livestock202 between about 40 mm and about 200 mm along the x-axis 904 isextending away from the laser 132. The portion of the profile signal 134from about −40 mm to about 200 mm along the x-axis 904 generallyrepresents a pattern similar to as previously described for the portionof the profile signal 134 between about −200 mm to about −40 mm alongthe x-axis 904. Again, the vision system 100 may be configured tointerpret this general pattern in the portion of the profile signal 134from about −40 mm to about 200 mm along the x-axis 940 as potentiallycomprising another teat 203.

Profile signals 134 may comprise one or more rising distance gradients908 and/or one or more falling distance gradients 910. A rising distancegradient 908 may indicate an increase in the distance between therobotic arm 200 and the dairy livestock 202 and may be generally definedas a change or transition from a first distance away from the roboticarm 200 to second distance that is further away from the robotic arm200. For example, the portion of the profile signal 134 from about −80mm to about −40 mm along the x-axis 940 may be referred to as a risingdistance gradient 908. A falling distance gradient 910 may indicate adecrease in the distance between the robotic arm 200 and the dairylivestock 202 and may be generally defined as a change or transitionfrom a first distance away from the robotic arm 200 to a second distancethat is closer to the robotic arm 200. For example, the portion of theprofile signal 134 from about −150 mm to about −125 mm along the x-axis904 may be referred to as a falling distance gradient 910.

The amount of distance change of a rising distance gradient 908 or afalling distance gradient 910 with respect to the z-axis 906 may bereferred to as a distance gradient length 916. A relatively largedistance gradient length 916 may indicate a large distance changebetween the robotic arm 200 and the dairy livestock 202 and a relativelysmall distance gradient length 916 may indicate a small distance changebetween the robotic arm 200 and the dairy livestock 202. For example,the magnitude of the distance gradient length 916 between a location atabout −150 mm to a location at about −125 mm along the x-axis 904 may beabout 125 mm.

A falling distance gradient 910 may be paired with a rising distancegradient 908 to form complementary distance gradients 912. The spacingbetween a falling distance gradient 910 and a corresponding risingdistance gradient 908 in a complementary distance gradient 912 may bereferred to as a complementary distance gradient spacing 918. Forexample, the portion of the profile signal 134 from about −150 mm toabout −40 mm along the x-axis 904 comprises a falling distance gradient910 (e.g. from about −150 mm to about −125 mm along the x-axis 904) anda rising distance gradient 908 (e.g. from about −80 mm to about −40 mmalong the x-axis 904) and may be referred to as a complementary distancegradient 912. The portion of the profile signal 134 between the fallingdistance gradient 910 and the rising distance gradient 908 (e.g. fromabout −125 mm to about −80 mm along the x-axis 904) may be referred toas the complementary distance gradient spacing 918.

Complementary distance gradients 912 may be used to identify edge paircandidates 914 that correspond with potential teats 203 of the dairylivestock 202. Edge pair candidates 914 may comprise a complementarydistance gradient 912 which may be used by the vision system 100 toidentify edge pairs 920 and/or teat candidates 442. Edge pair candidates914 comprise a falling distance gradient 910 that is paired with anadjacent rising distance gradient 908, which may generally describe theprofile or pattern for potential teats 203 of the dairy livestock 202.For example, the falling distance gradient 910 and the rising distancegradient 908 in the portion of the profile signal 134 from about −150 mmto about −40 mm along the x-axis 904 may be referred to as an edge paircandidate 914. An example of using edge pairs 920 to identify teats 203is described in FIG. 15.

The vision system 100 may compare the complementary distance gradients912 of each of the edge pair candidates 914 to a minimum distancegradient length 916 to be considered an edge pair 920 and to identifyone or more of the edge pair candidates 914 as an edge pair 920 when anedge pair candidate 914 has complementary distance gradient 912 lengthsgreater than or equal to the minimum distance gradient length 916 to thebe considered an edge pair 920. In other words, the vision system 100may analyze each edge pair candidate 914 to determine whether thegradient lengths 916 is sufficient to potentially be a teat 203 of thedairy livestock 202. Analyzing the gradient lengths 916 of edge paircandidates 914 allows the vision system 100 to eliminate false positivescaused by other features of the dairy livestock 202 such as skin folds.

The vision system 100 may be configured to apply one or more rules froma teat detection rule set 124 to edge pairs 920 to eliminate falsepositives of potential teats 203 and/or to identify teat candidates 442from among the one or more edge pairs 920. Examples of applying rulesfrom a teat detection rule set 124 are described in FIGS. 10 and 15.

The vision system 100 may be configured to determine and/or outputposition information for the one or more edge pairs 920 and/or one ormore teat candidates 442 identified in a profile signal 134. Forinstance, the vision system 100 may determine and/or output the positioninformation of the one or more edge pairs 920 and one or more teatcandidates 442 based on their Cartesian coordinates or polarcoordinates, for example, with respect to axis 904 and axis 906.

FIG. 10 is a flowchart of an embodiment of a teat detection method 1000using the vision system 100 with a profile signal 134. As an example, arobotic arm 200 may position itself adjacent to a dairy livestock 202within an access region 418 identified by the vision system 100. Thevision system 100 may employ method 1000 to use profile signals 134 toidentify one or more teat candidates 442 on the dairy livestock 202 andto determine position information associated with the identified teatcandidates 442.

At step 1002, the vision system 100 obtains a profile signal 134 of aleast a portion of a dairy livestock 202. In one embodiment, obtainingthe profile signal 134 comprises employing a laser 132 or a 3D camera136 to generate the profile signal 134. For example, the vision system100 may employ laser 132 to perform a laser scan of a portion of thedairy livestock 202 to generate the profile signal 134. As anotherexample, the vision system 100 may capture a 3D image 138 and mayprocess the 3D image 138 to generate the profile signal 134, forexample, similarly to as described in FIG. 4. In another embodiment,obtaining the profile signal 134 comprises obtaining a profile signal134 from a memory, for example, memory 104.

At step 1004, the vision system 100 detects one or more edge paircandidates 914 in the profile signal 134. For example, the vision system100 may identify complementary distance gradients 912 (i.e. fallingdistance gradient 910 and rising distant gradient 908 pairs) in theprofile signal 134 as edge pair candidates 914.

At step 1006, the vision system 100 compares complementary distancegradient 912 lengths for each of the edge pair candidates 914 to aminimum distance gradient length 916 to be considered an edge pair 920.In other words, the vision system 100 analyzes the complementarydistance gradient 912 lengths to remove complementary distance gradients912 that are too small be a teat 203 of the dairy livestock 202. Forexample, some complementary distance gradients 912 may be generated byother features of the dairy livestock 202 such as skinfolds. Comparingthe complementary distance gradients 912 to the minimum gradientdistance length 916 may reduce false positives when identifyingpotential teats 203 of a dairy livestock 202. The minimum distancegradient distance length 916 may be a fixed predetermined value or anadjustable value.

At step 1008, the vision system 100 identifies one or more edge pairs920 from among the one or more edge pair candidates 914 based on thecomparison. For example, the vision system 100 may identify one or moreof the edge pair candidates 914 as an edge pair 920 when an edge paircandidate 914 has complementary distance gradient 912 lengths greaterthan or equal to the minimum distance gradient length 916 to the beconsidered an edge pair 920.

At step 1010, the vision system 100 applies one or more rules from ateat detection rule set 124 to each of the one or more edge pairs 920 toidentify one or more teat candidates 442 from among the edge pairs 920.The teat detection rule set 124 may comprise one or more rules thatdefine criteria for an edge pair 920 to be considered a teat candidate442 for a teat 203 of the dairy livestock 202. The vision system 100 mayapply the one or more rules of the teat detection rule set 124 to eachof the edge pairs 920 to determine whether each edge pair 920 meets thecriteria to be considered a teat candidate 442. Non-limiting examples ofthe vision system 100 applying one or more rules from the teat detectionrule set 124 to edge pairs 920 are described below. At step 1012, thevision system 100 determined whether the edge pair 920 satisfies the oneor more rules of the teat detection rule set 124. The vision system 100may proceed to step 1014 when the edge pair 920 does not satisfy therules of the teat detection rule set 124. In other words, the visionsystem 100 may proceed to step 1014 when the edge pair 920 does not meetthe criteria to be considered a teat candidate 442 for a teat 203 of thedairy livestock 202. The vision system 100 may proceed to step 1016 whenthe edge pair 920 satisfies the rules of the teat detection rule set124. In other words, the vision system 100 may proceed to step 1016 whenthe edge pair 920 meets the criteria to be considered a teat candidate442 for a teat 203 of the dairy livestock 202. At step 1014, the visionsystem 100 may discard or reject edge pairs 920 in response todetermining that the edge pair 920 does not satisfy one or more rules ofthe teat detection rule set 124. At step 1016, the vision system 100 mayidentify an edge pair 920 as a teat candidate 442 in response todetermining that the edge pair 920 satisfies the one or more rules fromthe teat detection rule set 124.

As an example, the teat detection rule set 124 may comprise a rule thatindicates for the vision system 100 to compare the one or more edgepairs 920 to a teat model 128 and to discard edge pairs 920 that do notcorrespond with the teat model 128. In other words, the vision system100 may discard edge pairs 920 that vary in shape beyond some tolerancefrom the teat model 128. The vision system 100 may be configured toidentify an edge pair 920 from among the one or more edge pairs 920 as ateat candidate 442 when the edge pair 920 corresponds with the teatmodel 128. In general, the vision system 100 may apply such a rule todetermine whether an edge pair 920 has the general size and shape to beconsidered a teat candidate 442. The vision system 100 may reject andignore edge candidates 920 that are either too big, too small, or thewrong shape to be considered a teat candidate 442. An example ofcomparing a teat model 128 to edge pairs 920 to identify teat candidates442 is described in FIG. 15.

As another example, the teat detection rule set 124 may comprise a rulethat indicates a minimum or a maximum complementary distance gradientspacing 918 to be considered a teat candidate 442. For example, thevision system 100 may be configured to compare the complementarydistance gradient spacing 918 of each of the one or more edge pairs 920to a minimum complementary distance gradient spacing 918 to beconsidered a teat candidate 442. The vision system 100 may be configuredto discard edge pairs 920 with a complementary distance gradient spacing918 less than the minimum complementary distance gradient spacing 918 tobe considered a teat candidate 442. In other words, the vision system100 may discard or reject edge pairs 920 that are too narrow to be ateat candidate 442. The vision system 100 may be configured to identifyan edge pair 920 as a teat candidate 442 when the edge pair 920 has acomplementary distance gradient spacing 918 greater than or equal to theminimum complementary distance gradient spacing 918 to be considered ateat candidate 442. As another example, the vision system 100 may beconfigured to compare the complementary distance gradient spacing 918 ofeach of the one or more edge pairs 920 to a maximum complementarydistance gradient spacing 918 to be considered a teat candidate 442. Thevision system 100 may be configured to discard edge pairs 920 with acomplementary distance gradient spacing 918 greater than the maximumcomplementary distance gradient spacing 918 to be considered a teatcandidate 442. In other words, the vision system 100 may discard orreject edge pairs 920 that are too far apart to be a teat candidate 442.The vision system 100 may be configured to identify an edge pair 920 asa teat candidate 442 when the edge pair 920 has a complementary distancegradient spacing 918 less than or equal to the maximum complementarydistance gradient spacing 918 to be considered a teat candidate 442. Ingeneral, the vision system 100 may apply such rules to determine whetheran edge pair 920 has complementary gradient spacing 918 that likelycorresponds with a teat 203 of the dairy livestock 202. The visionsystem 100 may reject or ignore edge pairs 920 that are generated byfeatures of a dairy livestock 202 with a large complementary gradientspacing 918 such as an udder or with a small complementary gradientspacing 918 such as skin fold.

As another example, the teat detection rule set 124 may comprise a rulethat indicates a minimum or a maximum distance gradient length 916 to beconsidered a teat candidate 442. For example, the vision system 100 maybe configured to compare the distance gradient length 916 of each of theone or more edge pairs 920 to a minimum complementary distance gradientlength 916 to be considered a teat candidate 442. The vision system 100may be configured to discard edge pairs 920 with a distance gradientlength 916 less than the minimum distance gradient length 916 to beconsidered a teat candidate 442. In other words, the vision system 100may discard or reject edge pairs 920 that are too short to be a teatcandidate 442. The vision system 100 may be configured to identify anedge pair 920 as a teat candidate 442 when the edge pair 920 has adistance gradient length 916 greater than or equal to the minimumdistance gradient length 916 to be considered a teat candidate 442. Asanother example, the vision system 100 may be configured to compare thedistance gradient length 916 of each of the one or more edge pairs 920to a maximum complementary distance gradient length 916 to be considereda teat candidate 442. The vision system 100 may be configured to discardedge pairs 920 with a distance gradient length 916 greater than themaximum distance gradient length 916 to be considered a teat candidate442. In other words, the vision system 100 may discard or reject edgepairs 920 that are too long to be a teat candidate 442. The visionsystem 100 may be configured to identify an edge pair 920 as a teatcandidate 442 when the edge pair 920 has a distance gradient length 916less than or equal to the maximum distance gradient length 916 to beconsidered a teat candidate 442. In general, the vision system 100 mayapply such rules to determine whether each edge pair 920 has a distancegradient length 916 that likely corresponds with a teat 203 of the dairylivestock 202.

At step 1018, the vision system 100 determines whether there are anymoreedge pairs 920 to analyze. The vision system 100 may return to step 1010in response to determining that there are more edge pairs 920 toanalyze. In other words, the vision system 100 may continue to determinewhether the remaining edge pairs 920 meet the criteria to be considereda teat candidate 442. The vision system 100 may proceed to step 1020 inresponse to determining that there are no more edge pairs 920 toanalyze.

At step 1020, the vision system 100 determines position information forthe one or more teat candidates 442. In one embodiment, the visionsystem 100 may determine the position for the one or more teatcandidates 442 based on their respective positions with respect to theirlocation in the profile signal 134. For example referring to FIG. 9, theprofile signal 134 may define location of teat candidates 442 withrespect to a relative location of the robotic arm 200 and the laser 132,for example, point 902. The position of a teat candidate 442 may beindicated by a location in the profile signal 134 with respect to thex-axis 904 and the z-axis 906. For instance, the vision system 100 maydetermine a teat candidate 442 is located at about −100 mm with respectto the x-axis 904 and about 275 mm with respective the z-axis 906. Inother examples, the vision system 100 may determine the teat candidate442 is located within a general location, for example, between about−150 mm and about −75 mm with respect to the x-axis 904. In otherembodiments, the vision system 100 may determine position informationfor a teat candidate 442 with respect to one or more coordinate systems.For example, the vision system 100 may determine the location of a teatcandidate 442 within a profile signal 134 and may translate the locationof the teat candidate 442 into terms of another coordinate system suchas a coordinate system based on the location of the robot with aworkspace (e.g. stall 402). The vision system 100 may determine and/oroutput the position of the one or more teat candidates 442 based ontheir Cartesian coordinates, polar coordinates, or vectors with respectto the x-axis 302, the y-axis 306, and the z-axis 308, or any othersuitable technique as would be appreciated by one of ordinary skill inthe art upon viewing this disclosure. In some embodiments, the visionsystem 100 may output the position information for the one or more teatcandidates 442.

FIG. 11A is a position map 1100 of an embodiment of a plurality of teats203 of a dairy livestock 202. The vision system 100 may use the positionmap 1100 to identify teats 203 and/or to determine the location of teats203 of the dairy livestock 202 relative to a known reference point 1101.The teats 203 of the dairy livestock 202 are oriented in the positionmap 1100 with respect to the x-axis 304, the y-axis 306, and the z-axis308. Each teat 203 may be associated with a teat identifier 127 whichmay be any suitable identifier that uniquely identifies a teat 203. Forexample, a first teat 1102 may be associated with a teat identifier 127that corresponds with a front right teat 203 of the dairy livestock 202,a second teat 1104 may be associated with a teat identifier 127 thatcorresponds with a front left teat 203 of the dairy livestock 202, athird teat 1106 may be associated with a teat identifier 127 thatcorresponds with a rear right teat 203 of the dairy livestock 202, and afourth teat 1108 may be associated with a teat identifier 127 thatcorresponds with a rear left teat 203 of the dairy livestock 202.

In FIG. 11A, the first teat 1102 is located at a first location 1103,the second teat 1104 is located at a second location 1105, the thirdteat 1106 is located a third location 1107, and the fourth teat 1108 islocated a fourth location 1109 in the position map 1100. The locationsof the teats 203 may be an absolute location or a relative locationrelative to reference point 1101. The vision system 100 may use anysuitable reference point 1101 as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the positionof the teats 203 may be a relative position with respect to a referencepoint 1101 (e.g. a corner) in stall 402 or a relative position withrespect to a reference point 1101 (e.g. a base) of a robotic arm 200.The position of the teats 203 may be with respect to any reference point1101 as would be appreciated by one of ordinary skill in the art uponviewing this disclosure. The location of the teats 203 may be expressedor represented in any suitable units, for example, inches, centimeter,or millimeters.

In one embodiment, the location of teats 203 may be obtained fromposition information generated from processing a 3D image 138 and/or aprofile signal 134. For example, the location of teats 203 may beobtained from a method such as teat detection method 800 in FIG. 8 orteat detection method 1000 in FIG. 10. In another embodiment, thelocation of teats 203 may be obtained from a memory, for example, teatlocation information 130 in memory 104 or livestock information database114. For example, the location of the teats 203 may be obtained fromhistorical information for the location of teats 203 on a particulardairy livestock 202.

In another embodiment, the locations of teats 203 may be obtained fromone or more teat candidate clusters. A teat candidate cluster maycomprise a plurality of data points (e.g. a point cloud) that representpossible teat 203 locations. For example, teat candidate clusters may begenerated by the output from one or more teat detection processes (e.g.teat detection method 800 in FIG. 8 or teat detection method 1000 inFIG. 10) over multiple iterations. The position information for theteats 203 generated from each iteration of the one or more teatdetection processes may be aggregated or combined into a single dataset. The position information for each teat 203 may be a data pointwithin the single data set. The position information for a teat 203 maycomprise relative or absolute position information for the teat 203. Forexample, relative position information may describe the location of ateat 203 with respect to other teats 203. Absolute position informationmay described the location of a teat with respect to a known coordinatesystem. The data points corresponding to the teats 203 may form clustersaround the general location of teats 203, and these clusters of datapoints may be referred to as teat candidate clusters. The vision system100 may be configured to perform one or more operations on teatcandidate clusters to determine teat 203 locations. For example, thevision system 100 may be configured to average data points of a teatcandidate cluster to determine a teat 203 location. In other words, thevision system may be configured to approximate the location of a teat203 based on the data points in a teat candidate cluster. Other examplesof operations that the vision system 100 may perform on teat candidateclusters include, but are not limited to, finding a median data pointand filtering data points. An example of using teat candidate clustersis described in FIG. 16.

In one embodiment, the vision system 100 may be configured to apply oneor more offsets to the locations of the teats 203. Offsets may beapplied with respect to the x-axis 304, the y-axis 306, and/or thez-axis 308. For example, the vision system 100 may be configured todetermine a robot position offset 1120 between a center line of a dairylivestock 202 and base of the robotic arm 200. The vision system 100 maythen apply the robot position offset 1120 to the locations of the teats203 to shift the locations of the teats 203 by the robot position offset1120. In another example, the vision system 100 may be configured todetermine a dairy livestock position offset 1122 based on the locationof the dairy livestock 202 within a stall 402. For example, the dairylivestock position offset 1122 may be based on the position of the dairylivestock 202 with respect to a rear wall of the stall 402. The visionsystem 100 may then apply the dairy livestock position offset 1122 tothe locations of the teats 203 to shift the locations of the teats 203by the dairy livestock position offset 1122. Alternatively, any otherkind of offset may be applied to the locations of the teat 203.

In one embodiment, the vision system 100 may be configured to identifyan unknown teat 1110 using teat location information and/or the positionmap 1100. The vision system 100 may receive a teat position associatedwith an unknown teat 1110 and may then determined whether the unknownteat 1110 corresponds with the first teat 1102, the second teat 1104,the third teat 1106, or the fourth teat 1108. For instance, the visionsystem 100 may be configured to determine a first position distance 1112between the teat location of the unknown teat 1110 and the first teat1102, a second position distance 1114 between the teat location of theunknown teat 1110 and the second teat 1104, a third position distance1116 between the teat location of the unknown teat 1110 and the thirdteat 1106, and a fourth position distance 1118 between the teat locationof the unknown teat 1110 and the fourth teat 1108. The vision system 100may be configured to compare the first position distance 1112, thesecond position distance 1114, the third position distance 1116, and thefourth position distance 1116 to determine the identity of the unknownteat 1110. The vision system 100 may be configured to associate theunknown teat 1110 with a teat identifier 127 in response to identifyinga teat 203 that corresponds with the unknown teat 1110. For example, thevision system 100 may associate the unknown teat 1110 with a teatidentifier 127 corresponding with the first teat 1102, the second teat1104, the third teat 1106, and the fourth teat 1108 based on thecomparison of the first position distance 1112, the second positiondistance 1114, the third position distance 1116, and the fourth positiondistance 1116. An example of the vision system 100 identifying anunknown teat 1110 is described in FIG. 12A.

FIG. 11B is a position map 1120 of another embodiment of a plurality ofteats 203 of a dairy livestock 202. The vision system 100 may use theposition map 1120 to associate teat candidates 442 with teats 203 of thedairy livestock 202. The teats 203 of the dairy livestock 202 areoriented in the position map 1120 with respect to the x-axis 304, they-axis 306, and the z-axis 308. Each teat 203 may be associated with ateat identifier 127 which may be any suitable identifier that uniquelyidentifies a teat 203. For example, a first teat 1102 may be associatedwith a teat identifier 127 that corresponds with a front right teat 203of the dairy livestock 202, a second teat 1104 may be associated with ateat identifier 127 that corresponds with a front left teat 203 of thedairy livestock 203, a third teat 1106 may be associated with a teatidentifier 127 that corresponds with a rear right teat 203 of the dairylivestock, and a fourth teat 1108 with a teat identifier 127corresponding with a rear left teat 203 of the dairy livestock 202. Thelocation of the teats 203 may be determined similarly to as described inFIG. 11A.

One or more teat candidates 442 may be oriented in the position map 1120with respect to the x-axis 304, the y-axis 306, and the z-axis 308. Teatcandidate 442 position information may be obtained from the output ofone or more teat detection processes (e.g. teat detection method 800 inFIG. 8 or teat detection method 1000 in FIG. 10) or from a memory (e.g.memory 104). Teat candidate 442 position information may compriserelative or absolute position information for a teat candidate 442. Forexample, relative position information may describe the location of ateat candidate 442 with respect to other teat candidates 442 and/orteats 203. Absolute position information may describe the location of ateat candidate 442 with respect to a known coordinate system. In FIG.11B, a first teat candidate 1122 may be a first distance 1126 away froma target teat 203 (e.g. the second teat 1104) and a second teatcandidate 1124 may be a second distance 1128 away from the target teat203.

In one embodiment, the vision system 100 may be configured to associateor link teat candidates 442 with teats 203 of the dairy livestock 202.The vision system 100 may receive teat candidate position informationassociated with one or more teat candidates 442. The vision system 100may also receive a teat identifier 127 that indicates a target teat 203from the plurality of teats 203 (e.g. the first teat 1102, the secondteat 1104, the third teat 1106, and the fourth teat 1108) to associatewith one of the teat candidates 442. The vision system 100 may use theteat candidate position information to determine which of the teatcandidates 442 corresponds with the target teat 203. For example, thevision system 100 may determine which teat candidate 442 correspondswith the target teat 203 based on the distance a teat candidate 442 isaway from the target teat 203.

As another example, the vision system 100 may determine which teatcandidate 442 corresponds with the target teat 203 based on the positionof a teat candidate 442 with respect to other teat candidates 442 and/orwith respect the target teat 203. For instance, a teat candidate 442that is positioned as the left most (i.e. closest to the left side ofthe dairy livestock 202) teat candidate 442 along the x-axis 304 withrespect to other teat candidates 442 may be identified as the targetteat 203 when the target teat 203 is a left teat 203 (e.g. front leftteat 203 or rear left teat 203) of the dairy livestock 202. In FIG. 11B,the first teat candidate 1122 is the left most teat candidate 442compared to the second teat candidate 1124 and may be identified as thetarget teat 203. Similarly, a teat candidate 442 that is positioned asthe right most (i.e. closest to the right side of the dairy livestock202) teat candidate 442 along the x-axis 304 with respect to other teatcandidates 442 may be identified as the target teat 203 when the targetteat 203 is a right teat 203 (e.g. front right teat 203 or rear rightteat 203) of the dairy livestock 202. Alternatively, a teat candidate442 that is positioned as the forward or front most (i.e. closest to thehead of the dairy livestock 202) teat candidate 442 along the z-axis 308with respect to other teat candidates 442 may be identified as thetarget teat 203 when the target teat 203 is a front teat 203 (e.g. frontleft teat 203 or front right teat 203) of the dairy livestock 202.Alternatively, a teat candidate 442 that is positioned as the rear most(i.e. closest to the tail 201 of the dairy livestock 202) teat candidate442 along the z-axis 308 with respect to other teat candidates 442 maybe identified as the target teat 203 when the target teat 203 is a rearteat 203 (e.g. rear left teat 203 or rear right teat 203) of the dairylivestock 202.

In one embodiment, each teat 203 may be associated with a teat locationrange 1130 that indicates maximum distance a teat candidate 442 can beaway from a teat 203 to be considered the teat 203. For example, in FIG.11B, the first teat candidate 1122 is the left most teat candidate 442compared to the second teat candidate 1124 but the first teat candidate1122 is not located within the teat location range 1130 of the targetteat 203, and therefore, the second teat candidate 1124 may beidentified as the target teat 203.

As an example, the vision system 100 may obtain teat locationinformation 130 for a plurality of teats 203 on a dairy livestock 202 ina stall 402. The locations of the teats 203 may be obtained fromposition information generated by 3D images 138 and/or a profile signal134, or obtained from a memory (e.g. teat location information 130 inmemory 104 or livestock database 114). The vision system 100 may alsoobtain teat candidate position information associated with a pluralityof teat candidates 442 which may be obtain from processing a 3D image138 and/or a profile signal 134. For instance, the teat candidateposition for the plurality of teat candidates 442 may be obtained from amethod such as teat detection method 800 in FIG. 8 or teat detectionmethod 1000 in FIG. 10.

The vision system 100 may receive a teat identifier 127 for a targetteat 203 from among the plurality of teats 203 on the dairy livestock202. In one embodiment, the vision system 100 may receive the teatidentifier 127 from an operator that identifies the target teat 203. Inanother embodiment, the vision system 100 may determine which teat 203from the plurality of teats 203 is closest to the plurality of teatcandidates 442 and the teat identifier 127 for the determined teat 203may be used for the target teat 203. The teat identifier 127 indicateswhether the target teat 203 is a front right teat 203, a front left teat203, a rear right teat 203, or a rear left teat 203 of the dairylivestock 202. For example, in FIG. 11B the teat identifier 127 mayindicate that the target teat 203 is the front left teat 203 of thedairy livestock 202.

When the vision system 100 determines that the teat identifier 127corresponds with a left teat 203, the vision system 100 selects the leftmost teat candidate 442 from the plurality of teat candidates 442. Thevision system 100 may determine which teat candidate 442 from theplurality of teat candidates 442 is closest to the left side of thedairy livestock 202 based on its position (e.g. relative position orabsolute position) with respect to the other teat candidates 442 fromthe plurality of teat candidates 442. The vision system 100 may selectthe teat candidate 442 from the plurality of teat candidates 442 that isdetermined to be closest to the left side of the dairy livestock 202.For example, in FIG. 11B the vision system 100 may select the first teatcandidate 1122 as the left most teat candidate 442.

The vision system 100 may determine whether the selected teat candidate442 is within the teat location range 1130 for the target teat 203. Thevision system 100 may determine that the selected teat candidate 442 istoo far away from the target teat 203 to be considered the target teat203 when the distance between the selected teat candidate 442 and thetarget teat 203 is greater than or outside of the teat location range1130. A selected teat candidate 442 that is outside of the teat locationrange 1130 may indicate that the selected teat candidate 442 is a falsepositive and does not correspond with an actual teat candidate 442. Thevision system 100 may determine that the selected teat candidate 442 isa valid teat candidate 442 when the distance between the selected teatcandidate 442 and the target teat 203 is equal to, less than, or withinthe teat location range 1130. A selected teat candidate 442 that iswithin the teat location range 1130 may indicate that the selected teatcandidate 442 likely corresponds with the target teat 203. In FIG. 11B,the first teat candidate 1122 is outside of the teat location range 1130for the target teat 203.

When the vision system 100 determines that the selected teat candidate442 is outside of the teat location range 1130 of the target teat 203,the vision system 100 may select the next left most teat candidate 442from the plurality of teat candidates 442. In other words, the visionsystem 100 may continue searching for another left most teat candidate442 that is within the teat location range 1130 of the target teat 203,which is likely to correspond with the target teat 203. In general, thevision system 100 may iteratively select other teat candidates 442 thatare adjacent to the previously selected teat candidate 442 untilidentifying a selected teat candidate 442 that is within the teatlocation range 1130. For example, in FIG. 11B the vision system 100 mayselect the second teat candidate 1124 in response to the first teatcandidate 1122 being outside of the teat location range 1103 for thetarget teat 203.

When the vision system 100 determines that the selected teat candidate442 is within the teat location range 1130 of the target teat 203, thevision system 100 may determine that the selected teat candidate 442corresponds with the target teat 203 and may associate or link the teatidentifier 127 for the target teat 203 with the selected teat candidate442. In the example of FIG. 11B, the vision system 100 may determinethat the second teat candidate 442 corresponds with the target teat 203(i.e. the front left teat 203 of the dairy livestock 202). The visionsystem 100 may store the association between the selected teat candidate442 and the target teat 203 and/or any other information (e.g. positioninformation) associated with the selected teat candidate 442 in a memory(e.g. memory 104 or livestock information database 114). The positioninformation for the selected teat candidate 442 may be saved and/or usedby the vision system 100 for performing operations on the dairylivestock 200 associated with the selected teat candidate 442. In someembodiments, the vision system 100 may output the teat identifier 127and/or the teat candidate position that is associated with the selectedteat candidate 442.

A process similar to as described above may be performed when the visionsystem 100 determines that the teat identifier 127 corresponds with aright teat 203. Another example of the vision system associating a teatcandidate 442 with a teat 203 of the dairy livestock 202 is described inFIG. 12B.

FIG. 12A is a flowchart of an embodiment of a teat identification method1200 using the vision system 100. As an example, a robotic arm 200 maybe positioned adjacent to a dairy livestock 202 within an access region418 identified by the vision system 100. The vision system 100 mayemploy method 1200 to identify one or more teats 203 of the dairylivestock 202 and/or to determine the location of teats 203 of the dairylivestock 202.

At step 1202, the vision system 100 obtains teat location informationfor a plurality of teats 203 on a dairy livestock 202 in a stall 402.The locations of the teats 203 may be obtained from position informationgenerated from a 3D image 138 and/or a profile signal 134, obtained froma memory (e.g. teat location information 130 in memory 104 or livestockinformation database 114), or based on one or more teat candidateclusters similarly to as described in FIG. 11A. In one embodiment, theplurality of teats 203 may comprise a first teat 1102, a second teat1104, a third teat 1106, and a fourth teat 1108. For example, the firstteat 1102 may correspond with the front right teat 203, the second teat1104 may correspond with the front left teat 203, the third teat 1106may correspond with the rear right teat 203, and the fourth teat 1108may correspond with the rear left teat 203 of the dairy livestock 202.

At step 1204, the vision system 100 obtain a teat position associatedwith an unknown teat 1110. For example, the vision system 100 may obtainthe teat position for the unknown teat 1110 from position informationgenerated from processing a 3D image 138 and/or a profile signal 134.For example, the teat position for the unknown teat 1110 may be obtainedfrom a method such as teat detection method 800 in FIG. 8 or teatdetection method 1000 in FIG. 10.

At step 1206, the vision system 100 determines a first position distance1112 between the teat position of the unknown teat 1110 and the firstteat 1102 of the plurality of teats. The vision system 100 may determinethe distance between the unknown teat 1110 and the first teat 1102 bycalculating a difference between the teat position of the unknown teat1110 and the first teat 1102 with respect to the x-axis 304, the y-axis306, and/or the z-axis 308 as the first position distance 1112.

At step 1208, the vision system 100 determines a second positiondistance 1114 between the teat position of the unknown teat 1110 and thesecond teat 1104 of the plurality of teats. The vision system 100 maydetermine the distance between the unknown teat 1110 and the second teat1104 by calculating a difference between the teat position of theunknown teat 1110 and the second teat 1104 with respect to the x-axis304, the y-axis 306, and/or the z-axis 308 as the second positiondistance 1114.

At step 1210, the vision system 100 determines a third position distance1116 between the teat position of the unknown teat 1110 and the thirdteat 1106 of the plurality of teats. The vision system 100 may determinethe distance between the unknown teat 1110 and the third teat 1106 bycalculating a difference between the teat position of the unknown teat1110 and the third teat 1106 with respect to the x-axis 304, the y-axis306, and/or the z-axis 308 as the third position distance 1116.

At step 1212, the vision system 100 determines a fourth positiondistance 1118 between the teat position of the unknown teat 1110 and thefourth teat 1108 of the plurality of teats. The vision system 100 maydetermine the distance between the unknown teat 1110 and the fourth teat1108 by calculating a difference between the teat position of theunknown teat 1110 and the fourth teat 1108 with respect to the x-axis304, the y-axis 306, and/or the z-axis 308 as the fourth positiondistance 1118.

At step 1214, the vision system 100 compares the first position distance1112, the second position distance 1114, the third position distance1114, and the fourth position distance 1116 to each other in order todetermine which of the first position distance 1112, the second positiondistance 1114, the third position distance 1114, and the fourth positiondistance 1116 has the smallest position distance. In other words, thevision system 100 compares the first position distance 1112, the secondposition distance 1114, the third position distance 1114, and the fourthposition distance 1116 to determine which of the first teat 1102, thesecond teat 1104, the third teat 1106, and the fourth teat 1108 isclosest to the location of the unknown teat 1110. As an example, in FIG.11A the first position distance 1112 may be the smallest positiondistance and is closest to the unknown teat 1110.

At step 1216, the vision system 100 determines whether the firstposition distance 1112 has the smallest position distance which mayindicate that the first teat 1102 is the closest to the unknown teat1110. The vision system 100 may proceed to step 1218 when the visionsystem 100 determines that the first position distance 1112 has thesmallest position distance. Otherwise, the vision system 100 may proceedto step 1220 when the first position distance 1112 does not have thesmallest position distance.

At step 1218, the vision system 100 may associate a teat identifier 127for the unknown teat 1110 with the first teat 1102 in response todetermining that the first position distance 1112 has the smallestposition distance and may proceed to step 1230. For example, the visionsystem 100 may modify a teat identifier 127 for the unknown teat 1110 toindicate that the unknown teat 1110 is the first teat 1102.

Returning to step 1216, the vision system 100 may proceed to step 1220when the first position distance 1112 does not have the smallestposition distance. At step 1220, the vision system 100 determineswhether the second position distance 1114 has the smallest positiondistance which may indicate that the second teat 1104 is the closest tothe unknown teat 1110. The vision system 100 may proceed to step 1222when the vision system 100 determines that the second position distance1114 has the smallest position distance. Otherwise, the vision system100 may proceed to step 1224 when the second position distance 1114 doesnot have the smallest position distance.

At step 1222, the vision system 100 may associate a teat identifier 127for the unknown teat 1110 with the second teat 1104 in response todetermining that the second position distance 1114 has the smallestposition distance and may proceed to step 1230. For example, the visionsystem 100 may modify a teat identifier 127 for the unknown teat 1110 toindicate that the unknown teat 1110 is the second teat 1104.

Returning to step 1220, the vision system 100 may proceed to step 1224when the second position distance 1114 does not have the smallestposition distance. At step 1224, the vision system 100 determineswhether the third position distance 1116 has the smallest positiondistance which may indicate that the third teat 1106 is the closest tothe unknown teat 1110. The vision system 100 may proceed to step 1226when the vision system 100 determines that the third position distance1116 has the smallest position distance. Otherwise, the vision system100 may proceed to step 1228 when the third position distance 1116 doesnot have the smallest position distance.

At step 1226, the vision system 100 may associate a teat identifier 127for the unknown teat 1110 with the fourth teat 1108 and may proceed tostep 1230. For example, the vision system 100 may modify a teatidentifier 127 for the unknown teat 1110 to indicate that the unknownteat 1110 is the fourth teat 1108.

Referring again to FIG. 11A as an example, the vision system 100 maydetermine that the first teat 1102 has the smallest position distancefrom the teat position of the unknown teat 1110 and the vision system100 may modify a teat identifier 127 for the unknown teat 1110 toindicate that the unknown teat 1110 is the first teat 1102.

At step 1230, the vision system 100 stores the association between theteat identifier 127 and the teat position in a memory (e.g. memory 104or livestock information database 114). The vision system 100 may storethe modified teat identifier 127 for the unknown teat 110 and/or anyother information (e.g. position information) associated with theunknown teat 1110. The position information for the unknown teat 1110may be saved and/or used by the vision system 100 for performingoperations on the dairy livestock 202 associated with the unknown teat1110. In one embodiment, the vision system 100 may not store theassociation between the teat identifier 127 and the teat position in amemory and step 1220 may be omitted. In some embodiments, the visionsystem 100 may output the teat identifier 127 and/or the teat positionthat is associated with the unknown teat 1110.

FIG. 12B is a flowchart of another embodiment of a teat identificationmethod 1250 using the vision system 100. As an example, a robotic arm200 may be positioned adjacent to a dairy livestock 202 within an accessregion 418 identified by the vision system 100. The vision system 100may employ method 1250 to determine which teat candidate 442 from amonga plurality of teat candidates 442 corresponds with a target teat 203.

At step 1252, the vision system 100 determines the position of a firstteat candidate 442 relative to a second teat candidate 442. The positionof the first teat candidate 442 relative to the second teat candidate442 may be determined based on teat candidate position informationassociated with the first teat candidate 442 and the second teatcandidate 442. The teat candidate position information may be generatedfrom processing a 3D image 138 and/or a profile signal 134 and may bestored in memory 104. For instance, the teat candidate positioninformation may be obtained from a process such as teat detection method800 in FIG. 8 or teat detection method 1000 in FIG. 10. The visionsystem 100 may determine whether the first teat candidate 442 is at alocation to the left or to the right of the second teat candidate 442based on the teat candidate position information. In some embodiments,the vision system 100 may also determine whether the first teatcandidate 442 is in front of (i.e. closer to the head of the dairylivestock 202) or behind (i.e. closer to the rear of the dairy livestock202) the second teat candidate 442.

At step 1254, the vision system 100 proceeds to step 1256 in response todetermining that the first teat candidate 442 is to the left of thesecond teat candidate 442. Otherwise, the vision system 100 proceeds tostep 1258 in response to determining that the first teat candidate 442is not to the left of the second teat candidate 442. In other words, thevision system 100 proceeds to step 1258 in response to determining thatthe first teat candidate 442 is to the right of the second teatcandidate 442.

At step 1256, the vision system 100 assigns the first teat candidate 442as a left teat candidate 442 and assigns the second teat candidate 442as a right teat candidate 442 and proceeds to step 1260. The visionsystem 100 assigns the first teat candidate 442 as the left teatcandidate 442 in order to indicate that the first teat candidate 442 isthe left most teat candidate 442 out of the first teat candidate 442 andthe second teat candidate 442. Similarly, the vision system 100 assignsthe second teat candidate 442 as the right teat candidate 442 toindicate that the second teat candidate 442 is the right most teatcandidate 442 out of the first teat candidate 442 and the second teatcandidate 442.

At step 1258, the vision system 100 assigns the first teat candidate 442as a right teat candidate 442 and assigns the second teat candidate 442as a left teat candidate 442 and proceeds to step 1260. The visionsystem 100 assigns the first teat candidate 442 as the right teatcandidate 442 to indicate that the first teat candidate 442 is the rightmost teat candidate 442 out of the first teat candidate 442 and thesecond teat candidate 442. The vision system 100 assigns the second teatcandidate 442 as the left teat candidate 442 to indicate that the secondteat candidate 442 is the left most teat candidate 442 out of the firstteat candidate 442 and the second teat candidate 442.

At step 1260, the vision system 100 receives a teat identifier 127 for atarget teat 203 that indicates whether the target teat 203 is a leftteat 203 (i.e. a front left teat 203 or a rear left teat 203) or a rightteat 203 (i.e. a front right teat 203 or a rear right teat 203) of thedairy livestock 202. In one embodiment, the vision system 100 mayreceive the teat identifier 127 from an operator that identifies thetarget teat 203. In another embodiment, the vision system 100 maydetermine which teat 203 of the dairy livestock 202 is closest to thefirst teat candidate 442 and the second teat candidate 442 and the teatidentifier 127 for the determined teat 203 may be used for the targetteat 203. The teat identifier 127 may indicate whether the target teat203 is a front right teat 203, a front left teat 203, a rear right teat203, or a rear left teat 203 of the dairy livestock 202.

At step 1262, the vision system 100 proceeds to step 1264 in response tothe teat identifier 127 indicating that the target teat 203 is a leftteat 203 of the dairy livestock 202. Otherwise, the vision system 100proceeds to step 1266 in response to the teat identifier 127 indicatingthat the target teat 203 is a right teat 203 of the dairy livestock 202.

At step 1264, the vision system 100 determines whether the left teatcandidate 442 is within the teat location range 1130 of the target teat203. For example, the teat location range 1130 may indicate a maximumdistance a teat candidate 442 can be away from the target teat 203 to beconsidered the target teat 203. The vision system 100 may determine thedistance between the left teat candidate 442 and the target teat 203 andmay compare the determined distance to the maximum distance indicated bythe teat location range 1130. The vision system 100 may determine thatthe left teat candidate 442 is too far away from the target teat 203 tobe considered the target teat 203 when the distance between the leftteat candidate 442 and the target teat 203 is greater than or outside ofthe teat location range 1130 for the target teat 203. When the left teatcandidate 442 is outside of the teat location range 1130 may indicatethat that the left teat candidate 442 is a false positive and does notcorrespond with an actual teat 203 of the dairy livestock 202. In someembodiments, the vision system 100 may discard the left teat candidate442 in response to determining that the left teat candidate 442 isoutside of the teat location range 1130. The vision system 100 maydetermine that the left teat candidate 442 is a valid match for thetarget teat 203 when the distance between the left teat candidate 442and the target teat 203 is equal to, less than, or within the teatlocation range 1130 of the target teat 203. When the left teat candidate442 is within the teat location range 1130 may indicate that left teatcandidate 442 is likely the target 203.

At step 1268, the vision system 100 proceeds to step 1270 in response todetermining that the left teat candidate 442 is within the teat locationrange 1130 of the target teat 203. Otherwise, the vision system 100proceeds to step 1272 in response to determining that the left teatcandidate 442 is not within the teat location range 1130 of the targetteat 203.

At step 1270, the vision system 100 links the teat identifier 127 forthe target teat 203 with the left teat candidate 442 to identify theleft teat candidate 442 as the target teat 203. In one embodiment, thevision system 100 may store the relationship between the left teatcandidate 442 and the teat identifier 127 for the target teat 203 in amemory (e.g. memory 104 or livestock information database 114). Thevision system 100 may store also store any other information associatedwith the left teat candidate 442, for example, teat candidate positioninformation for the left teat candidate 442.

At step 1272, the vision system 100 links the teat identifier 127 forthe target teat 203 with the right teat candidate 442 to identify theright teat candidate 442 as the target teat 203. In one embodiment, thevision system 100 may store the relationship between the right teatcandidate 442 and the teat identifier 127 for the target teat 203 and/orany other information associated with the right teat candidate 442 in amemory (e.g. memory 104 or livestock information database 114).

Returning to step 1262, the vision system 100 proceeds to step 1266 inresponse to determining that the target teat 203 is a right teat 203 ofthe dairy livestock 202. At step 1266, the vision system 100 determineswhether the right teat candidate 442 is within the teat location range1130 of the target teat 203. The vision system 100 may determine whetherthe right teat candidate 442 is within the teat location range 1130 ofthe target teat 203 similarly to as described in step 1264.

At step 1274, the vision system 100 proceeds to step 1276 in response todetermining that the right teat candidate 442 is within the teatlocation range 1130 of the target teat 203. Otherwise, the vision system100 proceeds to step 1278 in response to determining that the right teatcandidate 442 is not within the teat location range 1130 of the targetteat 203.

At step 1276, the vision system 100 links the teat identifier 127 forthe target teat 203 with the right teat candidate 442 to identify theright teat candidate 442 as the target teat 203. In one embodiment, thevision system 100 may store the relationship between the right teatcandidate 442 and the teat identifier 127 for the target teat 203 and/orany other information associated with the right teat candidate 442 in amemory (e.g. memory 104 or livestock information database 114).

At step 1278, the vision system 100 links the teat identifier 127 forthe target teat 203 with the left teat candidate 442 to identify theleft teat candidate 442 as the target teat 203. In one embodiment, thevision system 100 may store the relationship between the left teatcandidate 442 and the teat identifier 127 for the target teat 203 and/orany other information associated with the left teat candidate 442 in amemory (e.g. memory 104 or livestock information database 114).

In another embodiment, teat identification method 1250 may be performedin a similar manner to determine whether a first teat candidate 442 or asecond teat candidate 442 can be linked to a teat identifier 127 of atarget teat 203 that is a front teat 203 (e.g. a front left teat 203 ora front right teat 203) or a rear teat 203 (e.g. a rear right teat 203or a rear left teat 203). In another embodiment, teat identificationmethod 1250 may be performed in a similar manner to determine whether afirst teat candidate 442 or a second teat candidate 442 can be linked toa teat identifier 127 of a particular target teat 203 (e.g. a front leftteat 203, a front right teat 203, a rear right teat 203 or a rear leftteat 203.

FIG. 13A is an embodiment of a comparison between a teat model 128 and afeature 1302 of a dairy livestock 202 in an image depth plane 302without a match. The teat model 128 may generally define a geometricshape that corresponds with the teat 203 of a dairy livestock 202 withinan image depth plane 302 of a 3D image 138. The vision system 100 maycompare the teat model 128 to features of the dairy livestock 202 todetermine whether any of the features correspond with a teat 203 or teatcandidate 442 of the dairy livestock 202. In FIG. 13A, feature 1302 mayrepresent a feature of the dairy livestock 202 that is beginning toappear within an image depth plane 302 as the vision system 100progressively advances through a plurality of image depth planes 302.The vision system 100 may compare the teat model 128 to the feature1302, but because the size and shape of the feature 1302 do not matchthe teat model 128 the vision system 100 may be unable to determinewhether or not the feature 1302 is a teat 203 of the dairy livestock202. The vision system 100 may compare the teat model 128 to the feature1302 in subsequent image depth planes 302 to determine whether thefeature 1302 corresponds with a teat 203 of the dairy livestock 202.

FIG. 13B is another embodiment of a comparison between a teat model 128and a feature 1302 of a dairy livestock 202 in an image depth plane 302with a match. Similar to FIG. 13A, the teat model 128 may generallydefine a geometric shape that corresponds with the teat 203 of a dairylivestock 202 within an image depth plane 302 of a 3D image 138. Thevision system 100 may compare the teat model 128 to features of thedairy livestock 202 to determine whether any of the features correspondwith a teat 203 or teat candidate 442 of the dairy livestock 202. InFIG. 13B, feature 1302 represents a feature of the dairy livestock 202that is more pronounced within an image depth plane 302 as the visionsystem 100 progressively advances through a plurality of image depthplanes 302. The vision system 100 may compare the teat model 128 to thefeature 1302 and may determine that the feature 1302 matches orcorresponds with the teat model 128 due to its similar size and shape asthe teat model 128. The vision system 100 may determine that the feature1302 corresponds with a teat candidate 442 or a teat 203 of the dairylivestock 202.

FIG. 14A is an embodiment of a comparison between a tail model 129 and atail candidate 712 in an image depth plane 302 without a match. The tailmodel 129 may generally define a geometric shape that corresponds withthe tail 201 of a dairy livestock 202 within an image depth plane 302 ofa 3D image 138. The vision system 100 may compare the tail model 129 tofeatures (e.g. tail candidates 712) of the dairy livestock 202 todetermine whether any of the features correspond with a tail 201 of thedairy livestock 202. In FIG. 14A, tail candidate 712 may represent afeature of the dairy livestock 202 that is beginning to appear within animage depth plane 302 as the vision system 100 progressively advancesthrough a plurality of image depth planes 302. The vision system 100 maycompare the tail model 129 to the tail candidate 712, but because thesize and shape of the tail candidate 712 do not match the tail model 129the vision system 100 may be unable to determine whether or not the tailcandidate 712 is the tail 201 of the dairy livestock 202. The visionsystem 100 may compare the tail model 129 to the tail candidate 712 insubsequent image depth planes 302 to determine whether the tailcandidate 712 corresponds with the tail 201 of the dairy livestock 202.

FIG. 14B is another embodiment of a comparisons between a tail model 129and a tail candidate 712 in an image depth plane 302 with a match.Similar to FIG. 14A, the tail model 129 may generally define a geometricshape that corresponds with the tail 201 of a dairy livestock 202 withinan image depth plane 302 of a 3D image 138. The vision system 100 maycompare the tail model 129 to features (e.g. tail candidates 712) of thedairy livestock 202 to determine whether any of the features correspondwith a tail 201 of the dairy livestock 202. In FIG. 14B, tail candidate712 represents a feature of the dairy livestock 202 that is morepronounced within an image depth plane 302 as the vision system 100progressively advances through a plurality of image depth planes 302.The vision system 100 may compare the tail model 129 to the tailcandidate 712 and may determine that the tail candidate 712 matches orcorresponds with the tail model 129 due to its similar size and shape asthe tail model 129. The vision system 100 may determine that the tailcandidate 712 corresponds with the tail 201 of the dairy livestock 202.

FIG. 15 is an embodiment of a comparison between a teat model 128 andedge pairs 920A, 920B, 920C, and 920D in a profile signal 134. The teatmodel 128 may generally define a shape or pattern that corresponds withteats 203 and/or teat candidates 442 of a dairy livestock 202. Thevision system 100 may compare the teat model 128 to edge pairs 920 alongthe profile signal 134 to identify teats 203 or teat candidates 442. Forexample, the vision system 100 may first compare the teat model 128 toan edge pair 920A. The vision system 100 may determine that the teatmodel 128 and the edge pair 920A differ too much, for example, beyond aset of tolerances, and that the edge pair 920A does not correspond witha teat 203 of the dairy livestock 202. Similarly, the vision system 100may compare the teat model 128 to an edge pair 920B and may determinethat the teat model 128 and the edge pair 920B differ too much and thatthe edge pair 920B does not correspond with a teat 203 of the dairylivestock 202. The vision system 100 may compare the teat model 128 toan edge pair 920C and may determine that the teat model 128 and the edgepair 920C are similar, for example, within a set of tolerance, and thatthe edge pair 920C corresponds with a teat 203 of the dairy livestock202. Similarly, vision system 100 may compare the teat model 128 to anedge pair 920D and may determine that the teat model 128 and the edgepair 920D are similar and that the edge pair 920D corresponds with ateat 203 of the dairy livestock 202.

FIG. 16 is a position map 1600 of an embodiment of teat candidateclusters 1602 for teats 203 of dairy livestock 202. The vision system100 may use teat candidate clusters 1602 to identify and/or to determinethe location of teats 203 of a dairy livestock 202. For example, thevision system 100 may use teat candidate clusters 1602 during a teatidentification process such as teat identification method 1100. The teatcandidate clusters 1602 are oriented in the position map 1600 withrespect to the x-axis 304, the y-axis 306, and the z-axis 308.

Each teat candidate cluster 1602 comprises a plurality of data points1604 that represent possible teat 203 locations. Teat candidate clusters1602 may be generated by the output of one or more teat detectionprocesses (e.g. teat detection method 800 or teat detection method 1000)over one or more iterations. Data points 1604 of the teat candidateclusters 1602 may form clusters around general locations 1606 of teats203. The vision system 100 may be configured to perform one or moreoperations on the data points 1604 of the teat candidate clusters 1602to determine the general locations 1606 of the teats 203. Examples ofoperations include, but are not limited to, averaging data points 1604within a teat candidate cluster 1602, finding a median data point 1604within a teat candidate cluster, and filtering out outlier data points1604. Any other suitable operation may be performed on the data points1604 of the teat candidate clusters 1602 to determine the generallocations 1606 of the teats 203 as would be appreciated by one ofordinary skill in the art upon viewing this disclosure.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

The invention claimed is:
 1. A vision system comprising: a three-dimensional (3D) camera configured to capture a 3D image of a rearview of a dairy livestock in a stall, wherein each pixel of the 3D image is associated with a depth value; and a processor operably coupled to the 3D camera, and configured to: obtain the 3D image; identify one or more regions within the 3D image comprising depth values greater than a depth value threshold; identify a thigh gap region among the one or more regions, wherein the thigh gap region comprises an area between hind legs of the dairy livestock; demarcate an access region within the thigh gap region, wherein the access region is defined by: a first vertical edge, a second vertical edge, a first upper edge spanning between the first vertical edge and the second vertical edge, and a first lower edge spanning between the first vertical edge and the second vertical edge; demarcate a tail detection region, wherein the tail detection region is defined by: a third vertical edge extending vertically from the first vertical edge of the access region, a fourth vertical edge extending vertically from the second vertical edge of the access region, a second upper edge spanning between the third vertical edge and the fourth vertical edge, and a second lower edge spanning between the third vertical edge and the fourth vertical edge, wherein the second lower edge is adjacent to the first upper edge of the access region; identify one or more tail candidates within the tail detection region; compare the one or more tail candidates to a tail model; identify a tail candidate from among the one or more tail candidates as a tail of the dairy livestock when the tail candidate corresponds with the tail model; and determine position information for the tail of the dairy livestock in response to identifying the tail of the dairy livestock.
 2. The system of claim 1, wherein: the tail model indicates a tail shape; and the tail of the dairy livestock corresponds with the tail shape in at least two of a plurality of depth planes.
 3. The system of claim 1, wherein: the system further comprises a memory configured to store a thigh gap detection rule set that identifies a marker positioned between the hind legs of the dairy livestock at a lower edge of the 3D image; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises identifying a region from among the one or more regions that comprises the marker as the thigh gap region.
 4. The system of claim 1, wherein: the system further comprises a memory configured to store a thigh gap detection rule set that indicates a minimum area value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the area of each of the one or more regions to the minimum area value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has an area greater than or equal to the minimum area value to be considered the thigh gap region.
 5. The system of claim 1, wherein: the system further comprises a memory configured to store a thigh gap detection rule set that indicates a maximum height value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the height of each of the one or more regions to the maximum height value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a height less than or equal to the maximum height value to be considered the thigh gap region.
 6. The system of claim 1, wherein: the system further comprises a memory configured to store a thigh gap detection rule set that indicates a minimum width value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the width of each of the one or more regions to the minimum width value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a width greater than or equal to the minimum width value to be considered the thigh gap region.
 7. An apparatus comprising: a memory operable to store three-dimensional (3D) images, wherein each pixel of the 3D image is associated with a depth value; and a processor operably coupled to the memory, and configured to: obtain a 3D image of a rearview of a dairy livestock in a stall; identify one or more regions within the 3D image comprising depth values greater than a depth value threshold; identify a thigh gap region among the one or more regions, wherein the thigh gap region comprises an area between hind legs of the dairy livestock; demarcate an access region within the thigh gap region, wherein the access region is defined by: a first vertical edge, a second vertical edge, a first upper edge spanning between the first vertical edge and the second vertical edge, and a first lower edge spanning between the first vertical edge and the second vertical edge; demarcate a tail detection region, wherein the tail detection region is defined by: a third vertical edge extending vertically from the first vertical edge of the access region, a fourth vertical edge extending vertically from the second vertical edge of the access region, a second upper edge spanning between the third vertical edge and the fourth vertical edge, and a second lower edge spanning between the third vertical edge and the fourth vertical edge, wherein the second lower edge is adjacent to the first upper edge of the access region; identify one or more tail candidates within the tail detection region; compare the one or more tail candidates to a tail model; and identify a tail candidate from among the one or more tail candidates as a tail of the dairy livestock when the tail candidate corresponds with the tail model; and determine position information for the tail of the dairy livestock in response to identifying the tail of the dairy livestock.
 8. The apparatus of claim 7, wherein: the tail model indicates a tail shape; and the tails of the dairy livestock corresponds with the tail shape in at least two of a plurality of depth planes.
 9. The apparatus of claim 7, wherein: the memory is operable to store a thigh gap detection rule set that identifies a marker positioned between the hind legs of the dairy livestock at a lower edge of the 3D image; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises identifying a region from among the one or more regions that comprises the marker as the thigh gap region.
 10. The apparatus of claim 7, wherein: the memory is operable to store a thigh gap detection rule set that indicates a minimum area value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the area of each of the one or more regions to the minimum area value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has an area greater than or equal to the minimum area value to be considered the thigh gap region.
 11. The apparatus of claim 7, wherein: the memory is operable to store a thigh gap detection rule set that indicates a maximum height value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the height of each of the one or more regions to the maximum height value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a height less than or equal to the maximum height value to be considered the thigh gap region.
 12. The apparatus of claim 7, wherein: the memory is operable to store a thigh gap detection rule set that indicates a minimum width value to be considered the thigh gap region; and identifying the thigh gap region comprises applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein applying the thigh gap detection rule set comprises: comparing the width of each of the one or more regions to the minimum width value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a width greater than or equal to the minimum width value to be considered the thigh gap region.
 13. A tail detection method comprising: obtaining, by a processor, a three-dimensional (3D) image of a rearview of a dairy livestock in a stall, wherein each pixel of the 3D image is associated with a depth value; identifying, by the processor, one or more regions within the 3D image comprising depth values greater than a depth value threshold; identifying, by the processor, a thigh gap region among the one or more regions, wherein the thigh gap region comprises an area between hind legs of the dairy livestock; demarcating, by the processor, an access region within the thigh gap region, wherein the access region is defined by: a first vertical edge, a second vertical edge, a first upper edge spanning between the first vertical edge and the second vertical edge, and a first lower edge spanning between the first vertical edge and the second vertical edge; demarcating, by the processor, a tail detection region, wherein the tail detection region is defined by: a third vertical edge extending vertically from the first vertical edge of the access region, a fourth vertical edge extending vertically from the second vertical edge of the access region, a second upper edge spanning between the third vertical edge and the fourth vertical edge, and a second lower edge spanning between the third vertical edge and the fourth vertical edge, wherein the second lower edge is adjacent to the first upper edge of the access region; identifying, by the processor, one or more tail candidates within an image depth plane; comparing, by the processor, the one or more tail candidates to a tail model; and identifying, by the processor, a tail candidate from among the one or more tail candidates as a tail of the dairy livestock when the tail candidate corresponds with the tail model; and determining, by the processor, position information for the tail of the dairy livestock in response to identifying the tail of the dairy livestock.
 14. The method of claim 13, wherein: the tail model indicates a tail shape; and the tail of the dairy livestock corresponds with the tail shape in at least two of a plurality of depth planes.
 15. The method of claim 13, wherein: demarcating the access region comprises reducing the width of the access region with respect to the x-axis, and reducing the width of the access region comprises: shifting the first vertical edge toward the second vertical edge; and shifting the second vertical edge toward the first vertical edge.
 16. The method of claim 13, wherein the tail model is an ellipse.
 17. The method of claim 13, further comprising applying, by the processor, a thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein: the thigh gap detection rule set identifies a marker positioned between the hind legs of the dairy livestock at a lower edge of the 3D image; and applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region comprises identifying a region from among the one or more regions that comprises the marker as the thigh gap region.
 18. The method of claim 13, further comprising applying, by the processor, a thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein: the thigh gap detection rule set indicates a minimum area value to be considered the thigh gap region; and applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region comprises: comparing the area of each of the one or more regions to the minimum area value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has an area greater than or equal to the minimum area value to be considered the thigh gap region.
 19. The method of claim 13, further comprising applying, by the processor, a thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein: the thigh gap detection rule set indicates a maximum height value to be considered the thigh gap region; and applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region comprises: comparing the height of each of the one or more regions to the maximum height value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a height less than or equal to the maximum height value to be considered the thigh gap region.
 20. The method of claim 13, further comprising applying, by the processor, a thigh gap detection rule set to the one or more regions to identify the thigh gap region, wherein: the thigh gap detection rule set indicates a minimum width value to be considered the thigh gap region; and applying the thigh gap detection rule set to the one or more regions to identify the thigh gap region comprises: comparing the width of each of the one or more regions to the minimum width value to be considered the thigh gap region; and identifying a region from among the one or more regions as the thigh gap region when the region has a width greater than or equal to the minimum width value to be considered the thigh gap region. 